Find preprocess workflow here.
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## ✓ tibble 3.0.4 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
## This is lavaan 0.6-8
## lavaan is FREE software! Please report any bugs.
##
## Attaching package: 'lavaan'
## The following object is masked from 'package:psych':
##
## cor2cov
## corrplot 0.84 loaded
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## id = col_character(),
## oth_text = col_character(),
## expoth_text = col_character(),
## sex = col_character(),
## edulvl1 = col_character(),
## spec1 = col_character(),
## edulvl2 = col_character(),
## spec2 = col_character(),
## jobfield = col_character(),
## jobpos = col_character(),
## city = col_character()
## )
## ℹ Use `spec()` for the full column specifications.
## tibble [495 × 133] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ id : chr [1:495] "00XzIUUVmQ" "0aABrq9MBY" "0c6myGTrKr" "0CS5iaAVos" ...
## $ e_dighelp : num [1:495] 5 NA 4 3.33 3 ...
## $ n_dighelp : num [1:495] 1 NA 1 3 2 1 3 2 4 1 ...
## $ e_socnet : num [1:495] 0 NA 3.2 3.6 4.5 ...
## $ f_socnet : num [1:495] 3 NA 2.4 1.6 2 ...
## $ n_socnet : num [1:495] 2 NA 5 5 2 2 2 4 3 4 ...
## $ gt_score : num [1:495] 2.67 2.67 2.83 2.5 2.67 ...
## $ pr01 : num [1:495] 3 3 3 3 3 4 3 1 3 4 ...
## $ pr02 : num [1:495] 3 3 3 3 3 3 3 1 3 3 ...
## $ pr03 : num [1:495] 3 3 3 3 3 1 5 1 3 4 ...
## $ pr04 : num [1:495] 2 3 3 3 2 0 3 2 4 3 ...
## $ pr05 : num [1:495] 3 2 1 2 3 4 4 1 0 3 ...
## $ pr06 : num [1:495] 2 2 4 3 3 5 4 3 4 4 ...
## $ pr07 : num [1:495] 3 3 3 3 3 5 3 2 1 3 ...
## $ pr08 : num [1:495] 2 3 4 2 3 4 4 4 3 4 ...
## $ pr09 : num [1:495] 2 4 3 3 4 4 4 3 3 4 ...
## $ pr10 : num [1:495] 2 3 3 2 3 4 3 3 1 3 ...
## $ co01 : num [1:495] 2 3 3 2 3 4 3 2 4 3 ...
## $ co02 : num [1:495] 3 3 3 2 3 3 3 1 2 3 ...
## $ co03 : num [1:495] 3 4 4 2 3 4 4 2 2 3 ...
## $ co04 : num [1:495] 4 2 4 4 3 5 4 3 5 4 ...
## $ co05 : num [1:495] 2 2 3 2 3 4 4 2 3 2 ...
## $ co06 : num [1:495] 3 3 4 4 3 3 4 2 2 3 ...
## $ co07 : num [1:495] 2 3 1 1 1 1 1 3 0 1 ...
## $ co08 : num [1:495] 2 2 2 1 4 5 2 1 2 1 ...
## $ co09 : num [1:495] 2 3 2 1 4 4 3 2 1 2 ...
## $ co10 : num [1:495] 3 2 3 2 3 5 3 1 1 2 ...
## $ ut01 : num [1:495] 3 4 4 5 4 5 5 4 4 3 ...
## $ ut02 : num [1:495] 2 3 3 4 4 5 5 3 3 3 ...
## $ ut03 : num [1:495] 3 2 4 5 1 5 3 4 3 3 ...
## $ ut04 : num [1:495] 3 3 3 4 4 5 4 3 2 3 ...
## $ ut05 : num [1:495] 3 2 3 5 4 3 4 3 4 3 ...
## $ ut06 : num [1:495] 2 3 4 4 4 5 4 3 3 3 ...
## $ ut07 : num [1:495] 3 2 4 5 4 4 4 3 4 3 ...
## $ ut08 : num [1:495] 3 3 3 4 4 5 4 3 4 4 ...
## $ ut09 : num [1:495] 2 3 3 3 3 4 4 3 3 4 ...
## $ ut10 : num [1:495] 2 2 2 4 1 2 2 1 3 3 ...
## $ ut11 : num [1:495] 2 2 3 4 3 2 4 1 1 3 ...
## $ ut12 : num [1:495] 3 4 4 3 4 2 4 3 3 4 ...
## $ fa01 : num [1:495] 2 2 3 4 2 2 3 3 2 2 ...
## $ fa02 : num [1:495] 2 3 2 4 2 0 2 3 2 1 ...
## $ fa03 : num [1:495] 3 3 3 1 4 2 1 0 3 2 ...
## $ fa04 : num [1:495] 2 3 3 2 1 2 2 0 1 2 ...
## $ fa05 : num [1:495] 3 3 3 3 4 4 3 3 2 3 ...
## $ fa06 : num [1:495] 3 2 4 3 3 2 4 0 2 3 ...
## $ fa07 : num [1:495] 3 3 3 3 1 3 2 1 1 3 ...
## $ fa08 : num [1:495] 3 2 2 2 1 2 2 3 2 2 ...
## $ fa09 : num [1:495] 3 2 3 4 2 1 2 3 2 2 ...
## $ fa10 : num [1:495] 2 2 2 4 1 3 3 4 4 2 ...
## $ de01 : num [1:495] 3 2 3 3 3 3 4 3 3 3 ...
## $ de02 : num [1:495] 3 3 3 3 3 4 4 2 1 3 ...
## $ de03 : num [1:495] 3 3 4 3 3 5 3 2 1 3 ...
## $ de04 : num [1:495] 2 3 2 0 2 2 0 3 1 3 ...
## $ de05 : num [1:495] 3 3 4 3 3 5 5 4 4 4 ...
## $ de06 : num [1:495] 2 3 3 3 3 3 4 0 0 3 ...
## $ de07 : num [1:495] 3 2 3 3 3 3 4 3 3 3 ...
## $ de08 : num [1:495] 3 3 3 3 3 5 4 1 1 3 ...
## $ de09 : num [1:495] 4 2 3 3 3 5 5 4 1 4 ...
## $ de10 : num [1:495] 3 3 4 3 3 3 4 3 3 3 ...
## $ de11 : num [1:495] 2 3 2 3 2 1 4 4 1 1 ...
## $ un01 : num [1:495] 3 3 3 2 4 3 4 3 4 4 ...
## $ un02 : num [1:495] 2 2 3 2 4 3 4 2 3 3 ...
## $ un03 : num [1:495] 3 1 3 3 4 5 4 2 4 3 ...
## $ un04 : num [1:495] 3 1 3 3 4 3 3 2 4 3 ...
## $ un05 : num [1:495] 3 3 4 3 4 4 4 3 4 4 ...
## $ un06 : num [1:495] 3 3 2 1 1 1 4 1 4 4 ...
## $ un07 : num [1:495] 2 3 2 2 4 4 3 1 2 3 ...
## $ un08 : num [1:495] 2 3 3 3 4 4 4 4 4 3 ...
## $ un09 : num [1:495] 2 1 4 2 4 4 4 1 2 4 ...
## $ un10 : num [1:495] 3 3 3 2 4 3 4 2 2 4 ...
## $ un11 : num [1:495] 3 2 3 2 4 4 3 3 4 3 ...
## $ un12 : num [1:495] 3 2 3 3 4 4 4 3 4 3 ...
## $ gt01 : num [1:495] 3 3 3 2 3 2 1 1 1 2 ...
## $ gt02 : num [1:495] 3 2 3 3 3 3 1 1 1 2 ...
## $ gt03 : num [1:495] 3 3 3 3 3 2 1 1 1 3 ...
## $ gt04 : num [1:495] 3 2 3 2 3 4 1 1 2 2 ...
## $ gt05 : num [1:495] 2 3 2 2 1 4 0 2 0 2 ...
## $ gt06 : num [1:495] 2 3 3 3 3 3 3 1 1 3 ...
## $ socnet : num [1:495] 1 0 1 1 1 1 1 1 1 1 ...
## $ vk : num [1:495] 1 -2 1 1 1 1 1 1 1 1 ...
## $ fb : num [1:495] 0 -2 1 1 1 0 0 1 0 1 ...
## $ tw : num [1:495] 0 -2 1 0 0 0 0 0 0 0 ...
## $ in : num [1:495] 1 -2 1 1 0 1 1 1 1 1 ...
## $ tt : num [1:495] 0 -2 0 1 0 0 0 0 0 0 ...
## $ yt : num [1:495] 0 -2 1 1 0 0 0 1 1 1 ...
## $ freqvk : num [1:495] 3 -2 3 2 3 3 3 3 3 3 ...
## $ freqfb : num [1:495] -2 -2 2 2 1 -2 -2 0 -2 3 ...
## $ freqtw : num [1:495] -2 -2 2 -2 -2 -2 -2 -2 -2 -2 ...
## $ freqin : num [1:495] 3 -2 3 1 -2 3 3 2 3 3 ...
## $ freqtt : num [1:495] -2 -2 -2 1 -2 -2 -2 -2 -2 -2 ...
## $ freqyt : num [1:495] -2 -2 2 2 -2 -2 -2 2 2 3 ...
## $ expvk : num [1:495] 0 -2 4 3 5 3 5 4 3 3 ...
## $ expfb : num [1:495] -2 -2 3 3 4 -2 -2 2 -2 2 ...
## $ exptw : num [1:495] -2 -2 2 -2 -2 -2 -2 -2 -2 -2 ...
## $ expin : num [1:495] 0 -2 4 4 -2 4 5 2 2 4 ...
## $ exptt : num [1:495] -2 -2 -2 4 -2 -2 -2 -2 -2 -2 ...
## $ expyt : num [1:495] -2 -2 3 4 -2 -2 -2 4 2 3 ...
## $ dighelp : num [1:495] 1 0 1 1 1 1 1 1 1 1 ...
## $ siri : num [1:495] 0 -2 0 1 0 0 1 0 1 0 ...
## [list output truncated]
## - attr(*, "spec")=
## .. cols(
## .. id = col_character(),
## .. e_dighelp = col_double(),
## .. n_dighelp = col_double(),
## .. e_socnet = col_double(),
## .. f_socnet = col_double(),
## .. n_socnet = col_double(),
## .. gt_score = col_double(),
## .. pr01 = col_double(),
## .. pr02 = col_double(),
## .. pr03 = col_double(),
## .. pr04 = col_double(),
## .. pr05 = col_double(),
## .. pr06 = col_double(),
## .. pr07 = col_double(),
## .. pr08 = col_double(),
## .. pr09 = col_double(),
## .. pr10 = col_double(),
## .. co01 = col_double(),
## .. co02 = col_double(),
## .. co03 = col_double(),
## .. co04 = col_double(),
## .. co05 = col_double(),
## .. co06 = col_double(),
## .. co07 = col_double(),
## .. co08 = col_double(),
## .. co09 = col_double(),
## .. co10 = col_double(),
## .. ut01 = col_double(),
## .. ut02 = col_double(),
## .. ut03 = col_double(),
## .. ut04 = col_double(),
## .. ut05 = col_double(),
## .. ut06 = col_double(),
## .. ut07 = col_double(),
## .. ut08 = col_double(),
## .. ut09 = col_double(),
## .. ut10 = col_double(),
## .. ut11 = col_double(),
## .. ut12 = col_double(),
## .. fa01 = col_double(),
## .. fa02 = col_double(),
## .. fa03 = col_double(),
## .. fa04 = col_double(),
## .. fa05 = col_double(),
## .. fa06 = col_double(),
## .. fa07 = col_double(),
## .. fa08 = col_double(),
## .. fa09 = col_double(),
## .. fa10 = col_double(),
## .. de01 = col_double(),
## .. de02 = col_double(),
## .. de03 = col_double(),
## .. de04 = col_double(),
## .. de05 = col_double(),
## .. de06 = col_double(),
## .. de07 = col_double(),
## .. de08 = col_double(),
## .. de09 = col_double(),
## .. de10 = col_double(),
## .. de11 = col_double(),
## .. un01 = col_double(),
## .. un02 = col_double(),
## .. un03 = col_double(),
## .. un04 = col_double(),
## .. un05 = col_double(),
## .. un06 = col_double(),
## .. un07 = col_double(),
## .. un08 = col_double(),
## .. un09 = col_double(),
## .. un10 = col_double(),
## .. un11 = col_double(),
## .. un12 = col_double(),
## .. gt01 = col_double(),
## .. gt02 = col_double(),
## .. gt03 = col_double(),
## .. gt04 = col_double(),
## .. gt05 = col_double(),
## .. gt06 = col_double(),
## .. socnet = col_double(),
## .. vk = col_double(),
## .. fb = col_double(),
## .. tw = col_double(),
## .. `in` = col_double(),
## .. tt = col_double(),
## .. yt = col_double(),
## .. freqvk = col_double(),
## .. freqfb = col_double(),
## .. freqtw = col_double(),
## .. freqin = col_double(),
## .. freqtt = col_double(),
## .. freqyt = col_double(),
## .. expvk = col_double(),
## .. expfb = col_double(),
## .. exptw = col_double(),
## .. expin = col_double(),
## .. exptt = col_double(),
## .. expyt = col_double(),
## .. dighelp = col_double(),
## .. siri = col_double(),
## .. alice = col_double(),
## .. salut = col_double(),
## .. oleg = col_double(),
## .. alex = col_double(),
## .. mia = col_double(),
## .. mts = col_double(),
## .. ggle = col_double(),
## .. oth = col_double(),
## .. oth_text = col_character(),
## .. expsiri = col_double(),
## .. expalice = col_double(),
## .. expsalut = col_double(),
## .. expoleg = col_double(),
## .. expalex = col_double(),
## .. expmia = col_double(),
## .. expmts = col_double(),
## .. expggle = col_double(),
## .. expoth = col_double(),
## .. expoth_text = col_character(),
## .. selfdrcar = col_double(),
## .. selfdrexp = col_double(),
## .. selfdrsafe = col_double(),
## .. eduai = col_double(),
## .. eduaiexp = col_double(),
## .. age = col_double(),
## .. sex = col_character(),
## .. edulvl1 = col_character(),
## .. spec1 = col_character(),
## .. edu2 = col_double(),
## .. edulvl2 = col_character(),
## .. spec2 = col_character(),
## .. jobfield = col_character(),
## .. jobpos = col_character(),
## .. city = col_character()
## .. )
Vectors of TAIA items:
pr_items <- colnames(taia)[8:17]
co_items <- colnames(taia)[18:27]
ut_items <- colnames(taia)[28:39]
fa_items <- colnames(taia)[40:49]
de_items <- colnames(taia)[50:60]
un_items <- colnames(taia)[61:72]
taia_items <- colnames(taia)[8:72]Vector of GT items:
Column names for further formatting:
col_names <- c("", "Num. of obs.", "Mean", "SD",
"Median", "Trimmed Mean", "MAD",
"Min", "Max", "Range",
"Skewness", "Kurtuosis", "SE")
total_colnames <- c("Alpha", "Standardized Alpha", "Guttman's Lambda 6",
"Average interitem correlation", "S/N",
"Alpha SE", "Scale Mean", "Total Score SD",
"Median interitem correlation")
item_stats_colnames <- c("Num. of Obs.", "Discrimination",
"Std Cor",
"Cor Overlap Corrected",
"Cor if drop",
"Difficulty", "SD")
alpha_drop_colnames <- c("Alpha", "Standardized Alpha",
"Guttman's Lambda 6", "Average interitem correlation",
"S/N", "Alpha SE", "Var(r)","Median interitem correlation")taia %>%
select(all_of(pr_items)) %>%
describe() %>%
kable(caption = "Predictability", label = 1, digits = 2, col.names = col_names)| Num. of obs. | Mean | SD | Median | Trimmed Mean | MAD | Min | Max | Range | Skewness | Kurtuosis | SE | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| pr01 | 1 | 495 | 2.84 | 0.99 | 3 | 2.87 | 1.48 | 0 | 5 | 5 | -0.28 | 0.37 | 0.04 |
| pr02 | 2 | 495 | 2.73 | 0.97 | 3 | 2.77 | 1.48 | 0 | 5 | 5 | -0.19 | 0.10 | 0.04 |
| pr03 | 3 | 495 | 2.89 | 1.01 | 3 | 2.91 | 1.48 | 0 | 5 | 5 | -0.15 | -0.05 | 0.05 |
| pr04 | 4 | 495 | 2.84 | 1.04 | 3 | 2.87 | 1.48 | 0 | 5 | 5 | -0.18 | -0.04 | 0.05 |
| pr05 | 5 | 495 | 2.22 | 1.20 | 2 | 2.22 | 1.48 | 0 | 5 | 5 | 0.03 | -0.31 | 0.05 |
| pr06 | 6 | 495 | 3.04 | 1.07 | 3 | 3.06 | 1.48 | 0 | 5 | 5 | -0.26 | 0.07 | 0.05 |
| pr07 | 7 | 495 | 2.59 | 1.11 | 3 | 2.61 | 1.48 | 0 | 5 | 5 | -0.15 | -0.10 | 0.05 |
| pr08 | 8 | 495 | 3.05 | 0.91 | 3 | 3.09 | 0.00 | 0 | 5 | 5 | -0.56 | 1.26 | 0.04 |
| pr09 | 9 | 495 | 2.89 | 0.95 | 3 | 2.94 | 0.00 | 0 | 5 | 5 | -0.50 | 1.05 | 0.04 |
| pr10 | 10 | 495 | 2.83 | 1.04 | 3 | 2.90 | 1.48 | 0 | 5 | 5 | -0.41 | 0.28 | 0.05 |
taia %>% select(all_of(pr_items)) %>%
pivot_longer(cols = all_of(pr_items)) %>%
ggplot(aes(value)) +
geom_bar(fill = "darkred") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Predictability") +
theme(plot.title = element_text(hjust = .5))## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?
pr01 OK
pr02 OK
pr03 OK
pr04 OK
pr05 positive skewness
pr06 OK
pr07 OK
pr08 high kurtosis
pr09 high kurtosis
pr10 OKtaia %>%
select(all_of(co_items)) %>%
describe() %>%
kable(caption = "Consistency", label = 2, digits = 2, col.names = col_names)| Num. of obs. | Mean | SD | Median | Trimmed Mean | MAD | Min | Max | Range | Skewness | Kurtuosis | SE | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| co01 | 1 | 495 | 2.49 | 1.08 | 3 | 2.51 | 1.48 | 0 | 5 | 5 | -0.17 | 0.13 | 0.05 |
| co02 | 2 | 495 | 2.51 | 1.04 | 3 | 2.53 | 1.48 | 0 | 5 | 5 | -0.19 | -0.06 | 0.05 |
| co03 | 3 | 495 | 2.86 | 1.02 | 3 | 2.92 | 1.48 | 0 | 5 | 5 | -0.40 | 0.31 | 0.05 |
| co04 | 4 | 495 | 3.47 | 1.09 | 4 | 3.54 | 1.48 | 0 | 5 | 5 | -0.57 | 0.32 | 0.05 |
| co05 | 5 | 495 | 2.20 | 1.11 | 2 | 2.17 | 1.48 | 0 | 5 | 5 | 0.10 | -0.17 | 0.05 |
| co06 | 6 | 495 | 2.52 | 1.11 | 3 | 2.53 | 1.48 | 0 | 5 | 5 | -0.13 | -0.15 | 0.05 |
| co07 | 7 | 495 | 1.59 | 1.13 | 2 | 1.52 | 1.48 | 0 | 5 | 5 | 0.54 | 0.12 | 0.05 |
| co08 | 8 | 495 | 1.90 | 1.04 | 2 | 1.86 | 1.48 | 0 | 5 | 5 | 0.40 | 0.28 | 0.05 |
| co09 | 9 | 495 | 2.05 | 1.07 | 2 | 2.01 | 1.48 | 0 | 5 | 5 | 0.35 | 0.12 | 0.05 |
| co10 | 10 | 495 | 2.44 | 1.10 | 2 | 2.44 | 1.48 | 0 | 5 | 5 | -0.04 | -0.06 | 0.05 |
taia %>% select(all_of(co_items)) %>%
pivot_longer(cols = all_of(co_items)) %>%
ggplot(aes(value)) +
geom_bar(fill = "chocolate3") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Consistency") +
theme(plot.title = element_text(hjust = .5))## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?
co01 OK
co02 OK
co03 high kurtosis
co04 high negative skewness
co05 positive skewness
co06 positive skewness
co07 high positive skewness
co08 positive skewness
co09positive skewness
co10 positive skewness
taia %>%
select(all_of(ut_items)) %>%
describe() %>%
kable(caption = "Utility", label = 3, digits = 2, col.names = col_names)| Num. of obs. | Mean | SD | Median | Trimmed Mean | MAD | Min | Max | Range | Skewness | Kurtuosis | SE | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ut01 | 1 | 495 | 3.78 | 1.05 | 4 | 3.88 | 1.48 | 0 | 5 | 5 | -0.86 | 1.12 | 0.05 |
| ut02 | 2 | 495 | 3.52 | 1.05 | 3 | 3.59 | 1.48 | 0 | 5 | 5 | -0.53 | 0.50 | 0.05 |
| ut03 | 3 | 495 | 3.56 | 1.11 | 4 | 3.64 | 1.48 | 0 | 5 | 5 | -0.57 | 0.15 | 0.05 |
| ut04 | 4 | 495 | 3.09 | 1.11 | 3 | 3.15 | 1.48 | 0 | 5 | 5 | -0.43 | 0.03 | 0.05 |
| ut05 | 5 | 495 | 3.05 | 1.21 | 3 | 3.09 | 1.48 | 0 | 5 | 5 | -0.33 | -0.15 | 0.05 |
| ut06 | 6 | 495 | 3.27 | 1.10 | 3 | 3.31 | 1.48 | 0 | 5 | 5 | -0.61 | 0.68 | 0.05 |
| ut07 | 7 | 495 | 3.20 | 1.13 | 3 | 3.23 | 1.48 | 0 | 5 | 5 | -0.28 | -0.22 | 0.05 |
| ut08 | 8 | 495 | 3.44 | 1.05 | 3 | 3.49 | 1.48 | 0 | 5 | 5 | -0.55 | 0.44 | 0.05 |
| ut09 | 9 | 495 | 3.18 | 1.17 | 3 | 3.23 | 1.48 | 0 | 5 | 5 | -0.47 | 0.16 | 0.05 |
| ut10 | 10 | 495 | 2.17 | 1.11 | 2 | 2.16 | 1.48 | 0 | 5 | 5 | 0.08 | -0.23 | 0.05 |
| ut11 | 11 | 495 | 2.67 | 1.23 | 3 | 2.69 | 1.48 | 0 | 5 | 5 | -0.11 | -0.41 | 0.06 |
| ut12 | 12 | 495 | 3.16 | 1.15 | 3 | 3.21 | 1.48 | 0 | 5 | 5 | -0.42 | 0.03 | 0.05 |
taia %>% select(all_of(ut_items)) %>%
pivot_longer(cols = all_of(ut_items)) %>%
ggplot(aes(value)) +
geom_bar(fill = "goldenrod3") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Utility") +
theme(plot.title = element_text(hjust = .5))## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?
ut01 extremely high negative skewness
ut02 high negative skewness
ut03 high negative skewness
ut04 negative skewness
ut05 OK
ut06 negative skewness
ut07 light negative skewness
ut08 negative skewness
ut09 negative skewness
ut10 positive skewness
ut11 OK
ut12 negative skewness
taia %>%
select(all_of(fa_items)) %>%
describe() %>%
kable(caption = "Faith", label = 4, digits = 2, col.names = col_names)| Num. of obs. | Mean | SD | Median | Trimmed Mean | MAD | Min | Max | Range | Skewness | Kurtuosis | SE | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| fa01 | 1 | 495 | 2.42 | 1.10 | 2 | 2.42 | 1.48 | 0 | 5 | 5 | -0.02 | -0.29 | 0.05 |
| fa02 | 2 | 495 | 2.16 | 1.18 | 2 | 2.15 | 1.48 | 0 | 5 | 5 | 0.18 | -0.42 | 0.05 |
| fa03 | 3 | 495 | 1.51 | 1.13 | 1 | 1.42 | 1.48 | 0 | 5 | 5 | 0.66 | 0.16 | 0.05 |
| fa04 | 4 | 495 | 1.57 | 1.08 | 1 | 1.51 | 1.48 | 0 | 5 | 5 | 0.55 | 0.17 | 0.05 |
| fa05 | 5 | 495 | 2.46 | 1.10 | 2 | 2.48 | 1.48 | 0 | 5 | 5 | -0.15 | -0.10 | 0.05 |
| fa06 | 6 | 495 | 2.47 | 1.08 | 3 | 2.49 | 1.48 | 0 | 5 | 5 | -0.18 | 0.06 | 0.05 |
| fa07 | 7 | 495 | 2.37 | 1.09 | 2 | 2.38 | 1.48 | 0 | 5 | 5 | -0.14 | -0.17 | 0.05 |
| fa08 | 8 | 495 | 2.21 | 1.14 | 2 | 2.17 | 1.48 | 0 | 5 | 5 | 0.28 | -0.13 | 0.05 |
| fa09 | 9 | 495 | 2.29 | 1.18 | 2 | 2.27 | 1.48 | 0 | 5 | 5 | 0.15 | -0.40 | 0.05 |
| fa10 | 10 | 495 | 2.64 | 1.20 | 3 | 2.62 | 1.48 | 0 | 5 | 5 | 0.06 | -0.28 | 0.05 |
taia %>% select(all_of(fa_items)) %>%
pivot_longer(cols = all_of(fa_items)) %>%
ggplot(aes(value)) +
geom_bar(fill = "darkgreen") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Faith") +
theme(plot.title = element_text(hjust = .5))## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?
fa01 OK
fa02 light positive skewness
fa03 hard positive skewness
fa04 hard positive skewness
fa05 OK
fa06 OK
fa07 OK
fa08 light positive skewness
fa09 OK
fa10 OK
taia %>%
select(all_of(de_items)) %>%
describe() %>%
kable(caption = "Dependability", label = 5, digits = 2, col.names = col_names)| Num. of obs. | Mean | SD | Median | Trimmed Mean | MAD | Min | Max | Range | Skewness | Kurtuosis | SE | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| de01 | 1 | 495 | 2.59 | 1.10 | 3 | 2.64 | 1.48 | 0 | 5 | 5 | -0.42 | 0.08 | 0.05 |
| de02 | 2 | 495 | 2.17 | 1.15 | 2 | 2.18 | 1.48 | 0 | 5 | 5 | 0.00 | -0.34 | 0.05 |
| de03 | 3 | 495 | 2.17 | 1.19 | 2 | 2.17 | 1.48 | 0 | 5 | 5 | 0.04 | -0.30 | 0.05 |
| de04 | 4 | 495 | 1.90 | 1.05 | 2 | 1.85 | 1.48 | 0 | 5 | 5 | 0.55 | 0.66 | 0.05 |
| de05 | 5 | 495 | 3.57 | 1.16 | 4 | 3.68 | 1.48 | 0 | 5 | 5 | -0.78 | 0.48 | 0.05 |
| de06 | 6 | 495 | 2.23 | 1.23 | 2 | 2.25 | 1.48 | 0 | 5 | 5 | 0.01 | -0.43 | 0.06 |
| de07 | 7 | 495 | 2.82 | 1.00 | 3 | 2.86 | 1.48 | 0 | 5 | 5 | -0.30 | 0.31 | 0.04 |
| de08 | 8 | 495 | 2.65 | 1.06 | 3 | 2.70 | 1.48 | 0 | 5 | 5 | -0.40 | 0.14 | 0.05 |
| de09 | 9 | 495 | 3.44 | 1.20 | 4 | 3.54 | 1.48 | 0 | 5 | 5 | -0.61 | -0.14 | 0.05 |
| de10 | 10 | 495 | 2.25 | 1.18 | 2 | 2.28 | 1.48 | 0 | 5 | 5 | -0.22 | -0.42 | 0.05 |
| de11 | 11 | 495 | 2.31 | 1.20 | 2 | 2.31 | 1.48 | 0 | 5 | 5 | 0.00 | -0.52 | 0.05 |
taia %>% select(all_of(de_items)) %>%
pivot_longer(cols = all_of(de_items)) %>%
ggplot(aes(value)) +
geom_bar(fill = "darkblue") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Dependability") +
theme(plot.title = element_text(hjust = .5))## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?
de01 negative skewness
de02 OK
de03 OK
de04 hard positive skewness
de05 hard negative skewness
de06 negative kurtosis
de07 OK
de08 OK
de09 negative skewness
de10 negative kurtosis
de11 negative kurtosis
taia %>%
select(all_of(un_items)) %>%
describe() %>%
kable(caption = "Understanding", label = 6, digits = 2, col.names = col_names)| Num. of obs. | Mean | SD | Median | Trimmed Mean | MAD | Min | Max | Range | Skewness | Kurtuosis | SE | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| un01 | 1 | 495 | 2.93 | 1.05 | 3 | 3.01 | 1.48 | 0 | 5 | 5 | -0.48 | 0.31 | 0.05 |
| un02 | 2 | 495 | 2.47 | 1.14 | 3 | 2.49 | 1.48 | 0 | 5 | 5 | -0.17 | -0.27 | 0.05 |
| un03 | 3 | 495 | 3.02 | 1.17 | 3 | 3.10 | 1.48 | 0 | 5 | 5 | -0.55 | 0.01 | 0.05 |
| un04 | 4 | 495 | 2.61 | 1.09 | 3 | 2.65 | 1.48 | 0 | 5 | 5 | -0.33 | -0.22 | 0.05 |
| un05 | 5 | 495 | 2.82 | 1.10 | 3 | 2.90 | 1.48 | 0 | 5 | 5 | -0.51 | 0.24 | 0.05 |
| un06 | 6 | 495 | 2.29 | 1.23 | 2 | 2.26 | 1.48 | 0 | 5 | 5 | 0.19 | -0.62 | 0.06 |
| un07 | 7 | 495 | 2.13 | 1.18 | 2 | 2.14 | 1.48 | 0 | 5 | 5 | -0.02 | -0.54 | 0.05 |
| un08 | 8 | 495 | 2.90 | 1.16 | 3 | 2.96 | 1.48 | 0 | 5 | 5 | -0.45 | 0.00 | 0.05 |
| un09 | 9 | 495 | 2.32 | 1.23 | 2 | 2.37 | 1.48 | 0 | 5 | 5 | -0.18 | -0.75 | 0.06 |
| un10 | 10 | 495 | 2.24 | 1.15 | 2 | 2.23 | 1.48 | 0 | 5 | 5 | 0.05 | -0.44 | 0.05 |
| un11 | 11 | 495 | 2.63 | 1.20 | 3 | 2.66 | 1.48 | 0 | 5 | 5 | -0.25 | -0.37 | 0.05 |
| un12 | 12 | 495 | 2.89 | 1.12 | 3 | 2.96 | 1.48 | 0 | 5 | 5 | -0.46 | 0.13 | 0.05 |
taia %>% select(all_of(un_items)) %>%
pivot_longer(cols = all_of(un_items)) %>%
ggplot(aes(value)) +
geom_bar(fill = "purple4") +
facet_wrap(~ name) +
scale_x_discrete(limits = 0:5) +
labs(x = "Score", y = "Number of observations",
title = "Understanding") +
theme(plot.title = element_text(hjust = .5))## Warning: Continuous limits supplied to discrete scale.
## Did you mean `limits = factor(...)` or `scale_*_continuous()`?
un01 OK
un02 OK
un03 negative skewness
un04 negative skewness
un05 positive kurtosis
un06 negative kurtosis
un07 negative kurtosis
un08 OK
un09 extra negative kurtosis
un10 negative kurtosis
un11 OK
un12 OK
pr1 <- psych::alpha(
taia %>% select(all_of(pr_items)),
cumulative = TRUE,
title = "Predictability Factor",
check.keys = FALSE
)kable(pr1$total,
caption = "Perdictability. Subscale statistics",
label = 7, digits = 2,
col.names = total_colnames
)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.81 | 0.81 | 0.82 | 0.3 | 4.25 | 0.01 | 27.92 | 6.23 | 0.33 |
pr1$item.stats$mean <- pr1$item.stats$mean / 5
kable(pr1$item.stats,
caption = "Predictability. Items statistics",
label = 8, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| pr01 | 495 | 0.79 | 0.80 | 0.80 | 0.72 | 0.57 | 0.99 |
| pr02 | 495 | 0.66 | 0.66 | 0.62 | 0.56 | 0.55 | 0.97 |
| pr03 | 495 | 0.45 | 0.45 | 0.37 | 0.31 | 0.58 | 1.01 |
| pr04 | 495 | 0.32 | 0.32 | 0.21 | 0.16 | 0.57 | 1.04 |
| pr05 | 495 | 0.61 | 0.59 | 0.51 | 0.46 | 0.44 | 1.20 |
| pr06 | 495 | 0.62 | 0.62 | 0.56 | 0.50 | 0.61 | 1.07 |
| pr07 | 495 | 0.74 | 0.73 | 0.70 | 0.63 | 0.52 | 1.11 |
| pr08 | 495 | 0.72 | 0.73 | 0.71 | 0.64 | 0.61 | 0.91 |
| pr09 | 495 | 0.61 | 0.62 | 0.56 | 0.50 | 0.58 | 0.95 |
| pr10 | 495 | 0.55 | 0.56 | 0.47 | 0.42 | 0.57 | 1.04 |
pr1$item.stats %>%
ggplot(aes(x = row.names(pr1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Predictability. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))kable(pr1$alpha.drop,
caption = "Predictability. Subscale statistics when item drop",
label = 9, digits = 2, col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| pr01 | 0.76 | 0.77 | 0.78 | 0.27 | 3.27 | 0.02 | 0.02 | 0.29 |
| pr02 | 0.78 | 0.79 | 0.80 | 0.29 | 3.66 | 0.01 | 0.03 | 0.32 |
| pr03 | 0.81 | 0.81 | 0.81 | 0.32 | 4.33 | 0.01 | 0.03 | 0.36 |
| pr04 | 0.82 | 0.83 | 0.82 | 0.35 | 4.78 | 0.01 | 0.02 | 0.36 |
| pr05 | 0.79 | 0.80 | 0.81 | 0.30 | 3.88 | 0.01 | 0.03 | 0.33 |
| pr06 | 0.79 | 0.79 | 0.81 | 0.30 | 3.79 | 0.01 | 0.03 | 0.33 |
| pr07 | 0.77 | 0.78 | 0.79 | 0.28 | 3.46 | 0.02 | 0.02 | 0.30 |
| pr08 | 0.77 | 0.78 | 0.79 | 0.28 | 3.46 | 0.02 | 0.02 | 0.30 |
| pr09 | 0.79 | 0.79 | 0.80 | 0.30 | 3.80 | 0.01 | 0.03 | 0.35 |
| pr10 | 0.80 | 0.80 | 0.81 | 0.31 | 3.99 | 0.01 | 0.03 | 0.35 |
kable(pr1$response.freq,
caption = "Predictability. Non missing response frequency for each item",
label = 10, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| pr01 | 0.02 | 0.06 | 0.24 | 0.45 | 0.19 | 0.04 | 0 |
| pr02 | 0.01 | 0.08 | 0.28 | 0.43 | 0.17 | 0.03 | 0 |
| pr03 | 0.01 | 0.06 | 0.26 | 0.40 | 0.22 | 0.05 | 0 |
| pr04 | 0.02 | 0.07 | 0.27 | 0.38 | 0.21 | 0.05 | 0 |
| pr05 | 0.09 | 0.17 | 0.33 | 0.29 | 0.09 | 0.03 | 0 |
| pr06 | 0.02 | 0.06 | 0.20 | 0.41 | 0.23 | 0.08 | 0 |
| pr07 | 0.04 | 0.12 | 0.28 | 0.38 | 0.14 | 0.04 | 0 |
| pr08 | 0.02 | 0.03 | 0.16 | 0.52 | 0.24 | 0.04 | 0 |
| pr09 | 0.02 | 0.05 | 0.18 | 0.53 | 0.17 | 0.04 | 0 |
| pr10 | 0.02 | 0.08 | 0.21 | 0.45 | 0.20 | 0.04 | 0 |
co1 <- psych::alpha(
taia %>% select(all_of(co_items)),
cumulative = TRUE,
title = "Consistency Factor",
check.keys = FALSE
)## Warning in psych::alpha(taia %>% select(all_of(co_items)), cumulative = TRUE, : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option
## Some items ( co07 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option
kable(co1$total,
caption = "Consistency. Subscale statistics",
label = 11, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.76 | 0.77 | 0.8 | 0.25 | 3.27 | 0.01 | 24.03 | 6.1 | 0.27 |
co1$item.stats$mean <- co1$item.stats$mean / 5
kable(co1$item.stats,
caption = "Consistency. Items statistics",
label = 12, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| co01 | 495 | 0.74 | 0.74 | 0.72 | 0.64 | 0.50 | 1.08 |
| co02 | 495 | 0.68 | 0.69 | 0.65 | 0.57 | 0.50 | 1.04 |
| co03 | 495 | 0.55 | 0.55 | 0.48 | 0.41 | 0.57 | 1.02 |
| co04 | 495 | 0.40 | 0.41 | 0.30 | 0.24 | 0.69 | 1.09 |
| co05 | 495 | 0.79 | 0.78 | 0.79 | 0.70 | 0.44 | 1.11 |
| co06 | 495 | 0.65 | 0.65 | 0.61 | 0.53 | 0.50 | 1.11 |
| co07 | 495 | -0.03 | -0.04 | -0.22 | -0.22 | 0.32 | 1.13 |
| co08 | 495 | 0.45 | 0.46 | 0.36 | 0.30 | 0.38 | 1.04 |
| co09 | 495 | 0.76 | 0.77 | 0.76 | 0.67 | 0.41 | 1.07 |
| co10 | 495 | 0.67 | 0.67 | 0.62 | 0.56 | 0.49 | 1.10 |
co1$item.stats %>%
ggplot(aes(x = row.names(co1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Consistency. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))kable(co1$alpha.drop,
caption = "Consistency. Subscale statistics when item drop",
label = 13, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| co01 | 0.71 | 0.72 | 0.76 | 0.22 | 2.52 | 0.02 | 0.06 | 0.26 |
| co02 | 0.72 | 0.73 | 0.77 | 0.23 | 2.66 | 0.02 | 0.06 | 0.26 |
| co03 | 0.74 | 0.75 | 0.79 | 0.25 | 2.98 | 0.02 | 0.07 | 0.27 |
| co04 | 0.77 | 0.77 | 0.80 | 0.27 | 3.36 | 0.01 | 0.06 | 0.35 |
| co05 | 0.70 | 0.71 | 0.75 | 0.21 | 2.42 | 0.02 | 0.06 | 0.26 |
| co06 | 0.73 | 0.73 | 0.77 | 0.23 | 2.74 | 0.02 | 0.06 | 0.27 |
| co07 | 0.83 | 0.82 | 0.83 | 0.34 | 4.70 | 0.01 | 0.03 | 0.36 |
| co08 | 0.76 | 0.76 | 0.79 | 0.26 | 3.23 | 0.02 | 0.07 | 0.34 |
| co09 | 0.71 | 0.71 | 0.75 | 0.22 | 2.47 | 0.02 | 0.06 | 0.26 |
| co10 | 0.72 | 0.73 | 0.77 | 0.23 | 2.69 | 0.02 | 0.07 | 0.26 |
kable(co1$response.freq,
caption = "Consistency. Non missing response frequency for each item",
label = 14, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| co01 | 0.05 | 0.11 | 0.31 | 0.39 | 0.11 | 0.03 | 0 |
| co02 | 0.03 | 0.12 | 0.32 | 0.38 | 0.13 | 0.02 | 0 |
| co03 | 0.02 | 0.07 | 0.22 | 0.44 | 0.21 | 0.04 | 0 |
| co04 | 0.01 | 0.04 | 0.10 | 0.35 | 0.32 | 0.18 | 0 |
| co05 | 0.06 | 0.19 | 0.37 | 0.27 | 0.09 | 0.02 | 0 |
| co06 | 0.04 | 0.14 | 0.28 | 0.38 | 0.13 | 0.03 | 0 |
| co07 | 0.18 | 0.31 | 0.32 | 0.14 | 0.04 | 0.02 | 0 |
| co08 | 0.08 | 0.27 | 0.42 | 0.16 | 0.06 | 0.01 | 0 |
| co09 | 0.06 | 0.24 | 0.40 | 0.22 | 0.06 | 0.02 | 0 |
| co10 | 0.04 | 0.14 | 0.33 | 0.34 | 0.11 | 0.03 | 0 |
ut1 <- psych::alpha(
taia %>% select(all_of(ut_items)),
cumulative = TRUE,
title = "Utility Factor",
check.keys = FALSE
)kable(ut1$total,
caption = "Utility. Subscale statistics",
label = 15, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.86 | 0.86 | 0.87 | 0.34 | 6.17 | 0.01 | 38.09 | 8.44 | 0.37 |
ut1$item.stats$mean <- ut1$item.stats$mean / 5
kable(ut1$item.stats,
caption = "Utility. Items statistics",
label = 16, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| ut01 | 495 | 0.77 | 0.78 | 0.78 | 0.71 | 0.76 | 1.05 |
| ut02 | 495 | 0.82 | 0.83 | 0.84 | 0.77 | 0.70 | 1.05 |
| ut03 | 495 | 0.57 | 0.57 | 0.52 | 0.47 | 0.71 | 1.11 |
| ut04 | 495 | 0.51 | 0.51 | 0.44 | 0.40 | 0.62 | 1.11 |
| ut05 | 495 | 0.69 | 0.69 | 0.65 | 0.61 | 0.61 | 1.21 |
| ut06 | 495 | 0.76 | 0.76 | 0.74 | 0.70 | 0.65 | 1.10 |
| ut07 | 495 | 0.63 | 0.63 | 0.59 | 0.54 | 0.64 | 1.13 |
| ut08 | 495 | 0.65 | 0.66 | 0.62 | 0.57 | 0.69 | 1.05 |
| ut09 | 495 | 0.68 | 0.68 | 0.64 | 0.59 | 0.64 | 1.17 |
| ut10 | 495 | 0.17 | 0.17 | 0.06 | 0.04 | 0.43 | 1.11 |
| ut11 | 495 | 0.56 | 0.55 | 0.48 | 0.45 | 0.53 | 1.23 |
| ut12 | 495 | 0.71 | 0.71 | 0.68 | 0.64 | 0.63 | 1.15 |
ut1$item.stats %>%
ggplot(aes(x = row.names(ut1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Utility. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))kable(ut1$alpha.drop,
caption = "Utility. Subscale statistics when item drop",
label = 17, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| ut01 | 0.84 | 0.84 | 0.85 | 0.32 | 5.16 | 0.01 | 0.03 | 0.34 |
| ut02 | 0.83 | 0.83 | 0.84 | 0.31 | 5.00 | 0.01 | 0.03 | 0.33 |
| ut03 | 0.85 | 0.85 | 0.86 | 0.35 | 5.84 | 0.01 | 0.04 | 0.41 |
| ut04 | 0.86 | 0.86 | 0.87 | 0.36 | 6.06 | 0.01 | 0.03 | 0.41 |
| ut05 | 0.84 | 0.84 | 0.86 | 0.33 | 5.45 | 0.01 | 0.04 | 0.37 |
| ut06 | 0.84 | 0.84 | 0.85 | 0.32 | 5.21 | 0.01 | 0.03 | 0.33 |
| ut07 | 0.85 | 0.85 | 0.86 | 0.34 | 5.65 | 0.01 | 0.04 | 0.37 |
| ut08 | 0.85 | 0.85 | 0.86 | 0.34 | 5.55 | 0.01 | 0.03 | 0.37 |
| ut09 | 0.84 | 0.85 | 0.86 | 0.33 | 5.49 | 0.01 | 0.03 | 0.37 |
| ut10 | 0.88 | 0.88 | 0.88 | 0.40 | 7.40 | 0.01 | 0.01 | 0.41 |
| ut11 | 0.85 | 0.86 | 0.87 | 0.35 | 5.92 | 0.01 | 0.04 | 0.41 |
| ut12 | 0.84 | 0.84 | 0.86 | 0.33 | 5.37 | 0.01 | 0.03 | 0.35 |
kable(ut1$response.freq,
caption = "Utility. Non missing response frequency for each item",
label = 18, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| ut01 | 0.01 | 0.01 | 0.06 | 0.29 | 0.34 | 0.28 | 0 |
| ut02 | 0.01 | 0.02 | 0.09 | 0.38 | 0.30 | 0.20 | 0 |
| ut03 | 0.01 | 0.04 | 0.09 | 0.33 | 0.31 | 0.22 | 0 |
| ut04 | 0.02 | 0.08 | 0.15 | 0.40 | 0.27 | 0.09 | 0 |
| ut05 | 0.03 | 0.06 | 0.20 | 0.35 | 0.23 | 0.12 | 0 |
| ut06 | 0.03 | 0.03 | 0.13 | 0.39 | 0.30 | 0.12 | 0 |
| ut07 | 0.01 | 0.05 | 0.19 | 0.35 | 0.26 | 0.14 | 0 |
| ut08 | 0.01 | 0.03 | 0.11 | 0.36 | 0.34 | 0.15 | 0 |
| ut09 | 0.03 | 0.05 | 0.15 | 0.38 | 0.25 | 0.13 | 0 |
| ut10 | 0.07 | 0.19 | 0.37 | 0.26 | 0.09 | 0.02 | 0 |
| ut11 | 0.05 | 0.12 | 0.27 | 0.32 | 0.18 | 0.07 | 0 |
| ut12 | 0.02 | 0.07 | 0.15 | 0.38 | 0.26 | 0.12 | 0 |
fa1 <- psych::alpha(
taia %>% select(all_of(fa_items)),
cumulative = TRUE,
title = "Faith Factor",
check.keys = FALSE
)kable(fa1$total,
caption = "Faith. Subscale statistics",
label = 19, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.77 | 0.77 | 0.81 | 0.25 | 3.26 | 0.02 | 22.1 | 6.41 | 0.24 |
fa1$item.stats$mean <- fa1$item.stats$mean / 5
kable(fa1$item.stats,
caption = "Faith. Items statistics",
label = 20, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| fa01 | 495 | 0.76 | 0.76 | 0.76 | 0.67 | 0.48 | 1.10 |
| fa02 | 495 | 0.61 | 0.60 | 0.56 | 0.47 | 0.43 | 1.18 |
| fa03 | 495 | 0.27 | 0.28 | 0.16 | 0.10 | 0.30 | 1.13 |
| fa04 | 495 | 0.41 | 0.42 | 0.33 | 0.26 | 0.31 | 1.08 |
| fa05 | 495 | 0.77 | 0.78 | 0.78 | 0.69 | 0.49 | 1.10 |
| fa06 | 495 | 0.55 | 0.56 | 0.50 | 0.42 | 0.49 | 1.08 |
| fa07 | 495 | 0.42 | 0.42 | 0.31 | 0.26 | 0.47 | 1.09 |
| fa08 | 495 | 0.58 | 0.57 | 0.52 | 0.44 | 0.44 | 1.14 |
| fa09 | 495 | 0.66 | 0.65 | 0.62 | 0.53 | 0.46 | 1.18 |
| fa10 | 495 | 0.63 | 0.62 | 0.57 | 0.50 | 0.53 | 1.20 |
fa1$item.stats %>%
ggplot(aes(x = row.names(fa1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Faith. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))kable(fa1$alpha.drop,
caption = "Faith. Subscale statistics when item drop",
label = 21, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| fa01 | 0.71 | 0.71 | 0.76 | 0.22 | 2.47 | 0.02 | 0.04 | 0.22 |
| fa02 | 0.74 | 0.74 | 0.78 | 0.24 | 2.87 | 0.02 | 0.04 | 0.24 |
| fa03 | 0.79 | 0.79 | 0.82 | 0.29 | 3.70 | 0.01 | 0.04 | 0.30 |
| fa04 | 0.77 | 0.77 | 0.81 | 0.27 | 3.30 | 0.02 | 0.04 | 0.26 |
| fa05 | 0.71 | 0.71 | 0.76 | 0.21 | 2.43 | 0.02 | 0.04 | 0.22 |
| fa06 | 0.75 | 0.75 | 0.79 | 0.25 | 2.95 | 0.02 | 0.05 | 0.29 |
| fa07 | 0.77 | 0.77 | 0.81 | 0.27 | 3.30 | 0.02 | 0.05 | 0.30 |
| fa08 | 0.74 | 0.75 | 0.79 | 0.25 | 2.92 | 0.02 | 0.04 | 0.24 |
| fa09 | 0.73 | 0.73 | 0.78 | 0.23 | 2.73 | 0.02 | 0.04 | 0.24 |
| fa10 | 0.74 | 0.74 | 0.79 | 0.24 | 2.79 | 0.02 | 0.04 | 0.24 |
kable(fa1$response.freq,
caption = "Faith. Non missing response frequency for each item",
label = 22, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| fa01 | 0.04 | 0.16 | 0.32 | 0.33 | 0.13 | 0.03 | 0 |
| fa02 | 0.08 | 0.21 | 0.36 | 0.21 | 0.12 | 0.02 | 0 |
| fa03 | 0.19 | 0.35 | 0.29 | 0.11 | 0.05 | 0.01 | 0 |
| fa04 | 0.16 | 0.34 | 0.33 | 0.12 | 0.04 | 0.01 | 0 |
| fa05 | 0.05 | 0.12 | 0.33 | 0.34 | 0.13 | 0.03 | 0 |
| fa06 | 0.05 | 0.12 | 0.32 | 0.38 | 0.11 | 0.03 | 0 |
| fa07 | 0.05 | 0.15 | 0.32 | 0.35 | 0.11 | 0.02 | 0 |
| fa08 | 0.05 | 0.20 | 0.38 | 0.23 | 0.10 | 0.03 | 0 |
| fa09 | 0.06 | 0.19 | 0.34 | 0.25 | 0.13 | 0.03 | 0 |
| fa10 | 0.04 | 0.12 | 0.31 | 0.32 | 0.14 | 0.08 | 0 |
de1 <- psych::alpha(
taia %>% select(all_of(de_items)),
cumulative = TRUE,
title = "Dependability Factor",
check.keys = FALSE
)## Warning in psych::alpha(taia %>% select(all_of(de_items)), cumulative = TRUE, : Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option
## Some items ( de04 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option
kable(de1$total,
caption = "Dependability. Subscale statistics",
label = 23, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.75 | 0.75 | 0.8 | 0.21 | 2.96 | 0.02 | 28.09 | 6.74 | 0.24 |
de1$item.stats$mean <- de1$item.stats$mean / 5
kable(de1$item.stats,
caption = "Dependability. Items statistics",
label = 24, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| de01 | 495 | 0.57 | 0.58 | 0.52 | 0.45 | 0.52 | 1.10 |
| de02 | 495 | 0.72 | 0.72 | 0.69 | 0.61 | 0.43 | 1.15 |
| de03 | 495 | 0.66 | 0.66 | 0.61 | 0.54 | 0.43 | 1.19 |
| de04 | 495 | -0.23 | -0.22 | -0.39 | -0.36 | 0.38 | 1.05 |
| de05 | 495 | 0.59 | 0.58 | 0.56 | 0.46 | 0.71 | 1.16 |
| de06 | 495 | 0.67 | 0.67 | 0.62 | 0.55 | 0.45 | 1.23 |
| de07 | 495 | 0.58 | 0.60 | 0.54 | 0.47 | 0.56 | 1.00 |
| de08 | 495 | 0.67 | 0.68 | 0.66 | 0.57 | 0.53 | 1.06 |
| de09 | 495 | 0.45 | 0.44 | 0.39 | 0.30 | 0.69 | 1.20 |
| de10 | 495 | 0.74 | 0.74 | 0.72 | 0.64 | 0.45 | 1.18 |
| de11 | 495 | 0.43 | 0.42 | 0.30 | 0.27 | 0.46 | 1.20 |
de1$item.stats %>%
ggplot(aes(x = row.names(de1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Dependability. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))kable(de1$alpha.drop,
caption = "Dependability. Subscale statistics when item drop",
label = 25, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| de01 | 0.73 | 0.72 | 0.78 | 0.21 | 2.59 | 0.02 | 0.07 | 0.22 |
| de02 | 0.71 | 0.70 | 0.76 | 0.19 | 2.32 | 0.02 | 0.07 | 0.22 |
| de03 | 0.72 | 0.71 | 0.77 | 0.20 | 2.44 | 0.02 | 0.07 | 0.23 |
| de04 | 0.82 | 0.82 | 0.84 | 0.31 | 4.49 | 0.01 | 0.02 | 0.32 |
| de05 | 0.73 | 0.72 | 0.76 | 0.21 | 2.59 | 0.02 | 0.07 | 0.22 |
| de06 | 0.71 | 0.71 | 0.77 | 0.19 | 2.42 | 0.02 | 0.07 | 0.22 |
| de07 | 0.73 | 0.72 | 0.78 | 0.20 | 2.56 | 0.02 | 0.07 | 0.22 |
| de08 | 0.71 | 0.70 | 0.76 | 0.19 | 2.39 | 0.02 | 0.06 | 0.22 |
| de09 | 0.75 | 0.74 | 0.78 | 0.22 | 2.88 | 0.02 | 0.07 | 0.30 |
| de10 | 0.70 | 0.69 | 0.76 | 0.19 | 2.28 | 0.02 | 0.06 | 0.22 |
| de11 | 0.75 | 0.75 | 0.80 | 0.23 | 2.93 | 0.02 | 0.08 | 0.32 |
kable(de1$response.freq,
caption = "Dependability. Non missing response frequency for each item",
label = 26, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| de01 | 0.05 | 0.11 | 0.24 | 0.43 | 0.14 | 0.03 | 0 |
| de02 | 0.09 | 0.17 | 0.36 | 0.26 | 0.10 | 0.02 | 0 |
| de03 | 0.10 | 0.17 | 0.35 | 0.27 | 0.09 | 0.03 | 0 |
| de04 | 0.07 | 0.26 | 0.45 | 0.14 | 0.05 | 0.02 | 0 |
| de05 | 0.02 | 0.03 | 0.10 | 0.28 | 0.34 | 0.23 | 0 |
| de06 | 0.10 | 0.17 | 0.32 | 0.28 | 0.11 | 0.03 | 0 |
| de07 | 0.02 | 0.06 | 0.27 | 0.42 | 0.20 | 0.03 | 0 |
| de08 | 0.04 | 0.10 | 0.24 | 0.44 | 0.15 | 0.03 | 0 |
| de09 | 0.01 | 0.06 | 0.13 | 0.27 | 0.33 | 0.20 | 0 |
| de10 | 0.10 | 0.15 | 0.30 | 0.34 | 0.10 | 0.02 | 0 |
| de11 | 0.07 | 0.20 | 0.28 | 0.31 | 0.12 | 0.03 | 0 |
un1 <- psych::alpha(
taia %>% select(all_of(un_items)),
cumulative = TRUE,
title = "Understanding Factor",
check.keys = FALSE
)kable(un1$total,
caption = "Understanding. Subscale statistics",
label = 27, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.92 | 0.92 | 0.92 | 0.5 | 12 | 0.01 | 31.24 | 10.15 | 0.51 |
un1$item.stats$mean <- un1$item.stats$mean / 5
kable(un1$item.stats,
caption = "Understanding. Items statistics",
label = 28, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| un01 | 495 | 0.75 | 0.75 | 0.73 | 0.70 | 0.59 | 1.05 |
| un02 | 495 | 0.84 | 0.84 | 0.84 | 0.81 | 0.49 | 1.14 |
| un03 | 495 | 0.59 | 0.59 | 0.54 | 0.51 | 0.60 | 1.17 |
| un04 | 495 | 0.76 | 0.76 | 0.73 | 0.71 | 0.52 | 1.09 |
| un05 | 495 | 0.81 | 0.81 | 0.80 | 0.77 | 0.56 | 1.10 |
| un06 | 495 | 0.57 | 0.56 | 0.50 | 0.48 | 0.46 | 1.23 |
| un07 | 495 | 0.72 | 0.72 | 0.69 | 0.66 | 0.43 | 1.18 |
| un08 | 495 | 0.76 | 0.76 | 0.75 | 0.71 | 0.58 | 1.16 |
| un09 | 495 | 0.73 | 0.73 | 0.69 | 0.67 | 0.46 | 1.23 |
| un10 | 495 | 0.75 | 0.75 | 0.72 | 0.69 | 0.45 | 1.15 |
| un11 | 495 | 0.79 | 0.79 | 0.77 | 0.74 | 0.53 | 1.20 |
| un12 | 495 | 0.75 | 0.76 | 0.73 | 0.70 | 0.58 | 1.12 |
un1$item.stats %>%
ggplot(aes(x = row.names(un1$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Understanding. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))kable(un1$alpha.drop,
caption = "Understanding. Subscale statistics when item drop",
label = 29, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| un01 | 0.91 | 0.92 | 0.91 | 0.50 | 10.87 | 0.01 | 0.01 | 0.51 |
| un02 | 0.91 | 0.91 | 0.91 | 0.48 | 10.26 | 0.01 | 0.01 | 0.50 |
| un03 | 0.92 | 0.92 | 0.92 | 0.52 | 12.05 | 0.01 | 0.01 | 0.54 |
| un04 | 0.91 | 0.92 | 0.92 | 0.50 | 10.82 | 0.01 | 0.01 | 0.51 |
| un05 | 0.91 | 0.91 | 0.91 | 0.49 | 10.46 | 0.01 | 0.01 | 0.50 |
| un06 | 0.92 | 0.92 | 0.92 | 0.53 | 12.30 | 0.01 | 0.01 | 0.54 |
| un07 | 0.92 | 0.92 | 0.92 | 0.50 | 11.10 | 0.01 | 0.01 | 0.51 |
| un08 | 0.91 | 0.92 | 0.91 | 0.50 | 10.80 | 0.01 | 0.01 | 0.51 |
| un09 | 0.92 | 0.92 | 0.92 | 0.50 | 11.06 | 0.01 | 0.01 | 0.51 |
| un10 | 0.91 | 0.92 | 0.92 | 0.50 | 10.93 | 0.01 | 0.01 | 0.51 |
| un11 | 0.91 | 0.91 | 0.91 | 0.49 | 10.62 | 0.01 | 0.01 | 0.50 |
| un12 | 0.91 | 0.92 | 0.92 | 0.50 | 10.86 | 0.01 | 0.01 | 0.50 |
kable(un1$response.freq,
caption = "Understanding. Non missing response frequency for each item",
label = 30, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| un01 | 0.02 | 0.07 | 0.19 | 0.43 | 0.24 | 0.05 | 0 |
| un02 | 0.05 | 0.14 | 0.29 | 0.35 | 0.14 | 0.03 | 0 |
| un03 | 0.03 | 0.08 | 0.16 | 0.36 | 0.29 | 0.07 | 0 |
| un04 | 0.03 | 0.13 | 0.24 | 0.40 | 0.17 | 0.02 | 0 |
| un05 | 0.04 | 0.08 | 0.19 | 0.44 | 0.21 | 0.04 | 0 |
| un06 | 0.06 | 0.24 | 0.28 | 0.25 | 0.14 | 0.04 | 0 |
| un07 | 0.09 | 0.21 | 0.30 | 0.29 | 0.09 | 0.02 | 0 |
| un08 | 0.04 | 0.09 | 0.18 | 0.39 | 0.23 | 0.06 | 0 |
| un09 | 0.08 | 0.18 | 0.25 | 0.30 | 0.17 | 0.01 | 0 |
| un10 | 0.06 | 0.20 | 0.33 | 0.27 | 0.12 | 0.02 | 0 |
| un11 | 0.05 | 0.13 | 0.23 | 0.36 | 0.18 | 0.05 | 0 |
| un12 | 0.03 | 0.08 | 0.19 | 0.41 | 0.23 | 0.06 | 0 |
omega(taia %>% ungroup() %>% select(all_of(taia_items)),
nfactors=6, p=.05, poly=FALSE,
digits=2, title="Omega", sl=TRUE, plot=TRUE, covar=FALSE)## Loading required namespace: GPArotation
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.94
## G.6: 0.97
## Omega Hierarchical: 0.6
## Omega H asymptotic: 0.62
## Omega Total 0.96
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* F4* F5* F6* h2 u2 p2
## pr01 0.57 0.34 0.26 0.57 0.43 0.58
## pr02 0.45 0.32 0.68 0.65
## pr03 0.20 0.54 0.39 0.61 0.10
## pr04 0.43 0.24 0.76 0.02
## pr05 0.56 0.45 0.52 0.48 0.60
## pr06 0.43 0.37 0.35 0.65 0.53
## pr07 0.57 0.24 0.25 0.21 0.50 0.50 0.66
## pr08 0.52 0.38 0.23 0.49 0.51 0.54
## pr09 0.41 0.29 0.29 0.71 0.57
## pr10 0.37 0.25 0.23 0.77 0.58
## co01 0.51 0.51 0.54 0.46 0.48
## co02 0.44 0.55 0.49 0.51 0.39
## co03 0.47 0.29 0.37 0.63 0.60
## co04 0.33 0.44 0.38 0.62 0.28
## co05 0.44 0.63 0.60 0.40 0.33
## co06 0.37 0.51 0.40 0.60 0.34
## co07- 0.29 0.17 0.83 0.11
## co08 0.34 -0.37 0.41 0.59 0.02
## co09 0.40 0.60 0.54 0.46 0.29
## co10 0.38 0.45 0.40 0.60 0.36
## ut01 0.36 0.67 0.61 0.39 0.21
## ut02 0.45 0.63 0.64 0.36 0.31
## ut03 0.22 0.32 0.52 0.54 0.46 0.09
## ut04 0.27 0.39 0.24 0.76 0.31
## ut05 0.41 0.44 0.37 0.63 0.45
## ut06 0.46 0.60 0.56 0.44 0.38
## ut07 0.34 0.46 0.34 0.66 0.35
## ut08 0.40 0.47 0.40 0.60 0.39
## ut09 0.42 0.46 0.39 0.61 0.44
## ut10 0.24 0.09 0.91 0.03
## ut11 0.54 0.21 0.39 0.48 0.52 0.61
## ut12 0.46 0.47 0.45 0.55 0.47
## fa01 0.44 0.61 0.60 0.40 0.33
## fa02 0.67 0.54 0.46 0.00
## fa03 -0.37 0.31 0.69 0.12
## fa04 0.35 0.32 0.21 0.40 0.60 0.32
## fa05 0.47 0.60 0.62 0.38 0.36
## fa06 0.58 0.30 0.52 0.48 0.65
## fa07 0.23 0.20 0.47 0.36 0.64 0.14
## fa08 0.61 0.49 0.51 0.00
## fa09 0.64 0.23 0.55 0.45 0.02
## fa10 0.24 0.63 0.48 0.52 0.12
## de01 0.43 0.29 0.71 0.63
## de02 0.57 0.23 0.30 0.49 0.51 0.67
## de03 0.50 0.28 0.38 0.62 0.65
## de04- 0.31 0.39 0.27 0.73 0.37
## de05 0.38 0.41 0.29 0.45 0.55 0.32
## de06 0.58 0.47 0.58 0.42 0.59
## de07 0.49 0.34 0.40 0.60 0.59
## de08 0.54 0.23 0.25 0.43 0.57 0.68
## de09 0.22 0.59 0.43 0.57 0.12
## de10 0.60 0.40 0.56 0.44 0.64
## de11 0.22 0.24 0.32 0.27 0.73 0.18
## un01 0.25 0.72 0.63 0.37 0.10
## un02 0.28 0.79 0.70 0.30 0.11
## un03 0.43 -0.26 0.36 0.64 0.10
## un04 0.25 0.68 0.53 0.47 0.12
## un05 0.28 0.77 0.67 0.33 0.11
## un06 -0.29 0.51 0.32 0.40 0.60 0.02
## un07 0.33 0.59 0.54 0.46 0.20
## un08 0.24 0.73 0.60 0.40 0.10
## un09 0.31 0.61 0.52 0.48 0.18
## un10 0.27 0.65 0.57 0.43 0.13
## un11 0.24 0.72 0.60 0.40 0.10
## un12 0.26 0.68 0.57 0.43 0.12
##
## With eigenvalues of:
## g F1* F2* F3* F4* F5* F6*
## 9.4 4.6 5.6 2.8 2.7 1.6 2.3
##
## general/max 1.68 max/min = 3.6
## mean percent general = 0.32 with sd = 0.22 and cv of 0.68
## Explained Common Variance of the general factor = 0.32
##
## The degrees of freedom are 1705 and the fit is 8.03
## The number of observations was 495 with Chi Square = 3753.55 with prob < 2.4e-155
## The root mean square of the residuals is 0.04
## The df corrected root mean square of the residuals is 0.04
## RMSEA index = 0.049 and the 10 % confidence intervals are 0.047 0.051
## BIC = -6825.22
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 2015 and the fit is 22.88
## The number of observations was 495 with Chi Square = 10774.78 with prob < 0
## The root mean square of the residuals is 0.15
## The df corrected root mean square of the residuals is 0.15
##
## RMSEA index = 0.094 and the 10 % confidence intervals are 0.092 0.096
## BIC = -1727.41
##
## Measures of factor score adequacy
## g F1* F2* F3* F4* F5*
## Correlation of scores with factors 0.81 0.89 0.97 0.86 0.94 0.71
## Multiple R square of scores with factors 0.66 0.79 0.94 0.74 0.88 0.50
## Minimum correlation of factor score estimates 0.32 0.59 0.88 0.47 0.76 0.00
## F6*
## Correlation of scores with factors 0.91
## Multiple R square of scores with factors 0.83
## Minimum correlation of factor score estimates 0.66
##
## Total, General and Subset omega for each subset
## g F1* F2* F3* F4* F5*
## Omega total for total scores and subscales 0.96 0.87 0.91 0.86 0.83 0.81
## Omega general for total scores and subscales 0.60 0.43 0.11 0.45 0.09 0.57
## Omega group for total scores and subscales 0.21 0.44 0.79 0.41 0.74 0.24
## F6*
## Omega total for total scores and subscales 0.51
## Omega general for total scores and subscales 0.14
## Omega group for total scores and subscales 0.37
splitHalf(taia %>% ungroup() %>% select(all_of(taia_items)),
raw=F, brute=F, n.sample=100, covar=F,
check.keys=F, key=NULL, use="pairwise")## Split half reliabilities
## Call: splitHalf(r = taia %>% ungroup() %>% select(all_of(taia_items)),
## raw = F, brute = F, n.sample = 100, covar = F, check.keys = F,
## key = NULL, use = "pairwise")
##
## Maximum split half reliability (lambda 4) = 0.96
## Guttman lambda 6 = 0.96
## Average split half reliability = 0.93
## Guttman lambda 3 (alpha) = 0.93
## Guttman lambda 2 = 0.94
## Minimum split half reliability (beta) = 0.86
## Average interitem r = 0.17 with median = 0.18
## Warning: Guttman has been deprecated. The use of the splitHalf function is
## recommended
## Warning in splitHalf(r): Some items were negatively correlated with total scale
## and were automatically reversed.
## Call: guttman(r = taia %>% ungroup() %>% select(all_of(taia_items)))
##
## Alternative estimates of reliability
##
## Guttman bounds
## L1 = 0.92
## L2 = 0.94
## L3 (alpha) = 0.93
## L4 (max) = 0.97
## L5 = 0.92
## L6 (smc) = 0.96
## TenBerge bounds
## mu0 = 0.93 mu1 = 0.94 mu2 = 0.94 mu3 = 0.94
##
## alpha of first PC = 0.95
## estimated greatest lower bound based upon communalities= 0.97
##
## beta found by splitHalf = 0.85
## $glb
## [1] 0.965817
##
## $communality
## pr01 pr02 pr03 pr04 pr05 pr06 pr07 pr08
## 0.7253391 0.5393523 0.4690491 0.6292470 0.7830601 0.6280428 0.5758103 0.6112839
## pr09 pr10 co01 co02 co03 co04 co05 co06
## 0.5896570 0.3330770 0.5758243 0.6305638 0.4895706 0.4890165 0.7521395 0.5726406
## co07 co08 co09 co10 ut01 ut02 ut03 ut04
## 0.4356987 0.4665793 0.6337356 0.4811587 0.7718201 0.7277348 0.6422830 0.3722353
## ut05 ut06 ut07 ut08 ut09 ut10 ut11 ut12
## 0.5234489 0.7171434 0.5757501 0.5792722 0.7253062 0.3644927 0.6451266 0.5389414
## fa01 fa02 fa03 fa04 fa05 fa06 fa07 fa08
## 0.6943597 0.5928412 0.4376811 0.5197275 0.7565165 0.5844505 0.4786126 0.6061162
## fa09 fa10 de01 de02 de03 de04 de05 de06
## 0.6945327 0.4919291 0.4216417 0.5711187 0.5654506 0.4521109 0.7942688 0.7264322
## de07 de08 de09 de10 de11 un01 un02 un03
## 0.5278337 0.5885239 0.6875419 0.6514868 0.3425354 0.6718202 0.7379031 0.4402195
## un04 un05 un06 un07 un08 un09 un10 un11
## 0.6398019 0.6914535 0.4729219 0.5759967 0.6904648 0.5762576 0.6507091 0.6870705
## un12
## 0.6276157
##
## $numf
## [1] 19
##
## $Call
## glb.fa(r = taia %>% ungroup() %>% select(all_of(taia_items)))
Excluded items: co07, ut10, de04
Reason: negative discrimination
Stems:
co07 Я могу сказать, что система работает корректно, только протестировав её (R)ut10 Мне кажется, при оформлении услуг через интернет можно справиться и без цифровых помощников (R)de04 Я могу доверять интеллектуальной системе, если я точно понимаю, какие опасности исходят от неё (R)co2 <- psych::alpha(
taia %>% select(all_of(co_items)),
cumulative = TRUE,
title = "Consistency Factor",
check.keys = FALSE
)kable(co2$total,
caption = "Consistency. Subscale statistics",
label = 11, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.83 | 0.82 | 0.83 | 0.34 | 4.7 | 0.01 | 22.44 | 6.24 | 0.36 |
co2$item.stats$mean <- co2$item.stats$mean / 5
kable(co2$item.stats,
caption = "Consistency. Items statistics",
label = 12, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| co01 | 495 | 0.74 | 0.74 | 0.71 | 0.65 | 0.50 | 1.08 |
| co02 | 495 | 0.72 | 0.72 | 0.67 | 0.62 | 0.50 | 1.04 |
| co03 | 495 | 0.56 | 0.57 | 0.48 | 0.43 | 0.57 | 1.02 |
| co04 | 495 | 0.44 | 0.43 | 0.33 | 0.28 | 0.69 | 1.09 |
| co05 | 495 | 0.79 | 0.78 | 0.78 | 0.70 | 0.44 | 1.11 |
| co06 | 495 | 0.68 | 0.67 | 0.62 | 0.56 | 0.50 | 1.11 |
| co08 | 495 | 0.44 | 0.44 | 0.34 | 0.29 | 0.38 | 1.04 |
| co09 | 495 | 0.76 | 0.76 | 0.75 | 0.67 | 0.41 | 1.07 |
| co10 | 495 | 0.68 | 0.68 | 0.62 | 0.57 | 0.49 | 1.10 |
co2$item.stats %>%
ggplot(aes(x = row.names(co2$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Consistency. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))kable(co2$alpha.drop,
caption = "Consistency. Subscale statistics when item drop",
label = 13, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| co01 | 0.79 | 0.79 | 0.80 | 0.32 | 3.81 | 0.01 | 0.03 | 0.34 |
| co02 | 0.80 | 0.80 | 0.81 | 0.33 | 3.89 | 0.01 | 0.03 | 0.35 |
| co03 | 0.82 | 0.82 | 0.82 | 0.36 | 4.49 | 0.01 | 0.03 | 0.41 |
| co04 | 0.84 | 0.83 | 0.83 | 0.39 | 5.04 | 0.01 | 0.02 | 0.41 |
| co05 | 0.79 | 0.79 | 0.79 | 0.31 | 3.66 | 0.01 | 0.02 | 0.33 |
| co06 | 0.80 | 0.80 | 0.81 | 0.34 | 4.06 | 0.01 | 0.03 | 0.35 |
| co08 | 0.83 | 0.83 | 0.83 | 0.39 | 5.01 | 0.01 | 0.02 | 0.40 |
| co09 | 0.79 | 0.79 | 0.80 | 0.32 | 3.74 | 0.01 | 0.02 | 0.34 |
| co10 | 0.80 | 0.80 | 0.81 | 0.34 | 4.04 | 0.01 | 0.03 | 0.35 |
kable(co2$response.freq,
caption = "Consistency. Non missing response frequency for each item",
label = 14, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| co01 | 0.05 | 0.11 | 0.31 | 0.39 | 0.11 | 0.03 | 0 |
| co02 | 0.03 | 0.12 | 0.32 | 0.38 | 0.13 | 0.02 | 0 |
| co03 | 0.02 | 0.07 | 0.22 | 0.44 | 0.21 | 0.04 | 0 |
| co04 | 0.01 | 0.04 | 0.10 | 0.35 | 0.32 | 0.18 | 0 |
| co05 | 0.06 | 0.19 | 0.37 | 0.27 | 0.09 | 0.02 | 0 |
| co06 | 0.04 | 0.14 | 0.28 | 0.38 | 0.13 | 0.03 | 0 |
| co08 | 0.08 | 0.27 | 0.42 | 0.16 | 0.06 | 0.01 | 0 |
| co09 | 0.06 | 0.24 | 0.40 | 0.22 | 0.06 | 0.02 | 0 |
| co10 | 0.04 | 0.14 | 0.33 | 0.34 | 0.11 | 0.03 | 0 |
ut2 <- psych::alpha(
taia %>% select(all_of(ut_items)),
cumulative = TRUE,
title = "Utility Factor",
check.keys = FALSE
)kable(ut2$total,
caption = "Utility. Subscale statistics",
label = 15, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.88 | 0.88 | 0.88 | 0.4 | 7.4 | 0.01 | 35.92 | 8.32 | 0.41 |
ut2$item.stats$mean <- ut2$item.stats$mean / 5
kable(ut2$item.stats,
caption = "Utility. Items statistics",
label = 16, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| ut01 | 495 | 0.79 | 0.79 | 0.79 | 0.73 | 0.76 | 1.05 |
| ut02 | 495 | 0.83 | 0.84 | 0.84 | 0.79 | 0.70 | 1.05 |
| ut03 | 495 | 0.55 | 0.56 | 0.49 | 0.45 | 0.71 | 1.11 |
| ut04 | 495 | 0.53 | 0.53 | 0.46 | 0.42 | 0.62 | 1.11 |
| ut05 | 495 | 0.69 | 0.68 | 0.64 | 0.60 | 0.61 | 1.21 |
| ut06 | 495 | 0.77 | 0.77 | 0.75 | 0.71 | 0.65 | 1.10 |
| ut07 | 495 | 0.62 | 0.62 | 0.57 | 0.52 | 0.64 | 1.13 |
| ut08 | 495 | 0.66 | 0.67 | 0.62 | 0.58 | 0.69 | 1.05 |
| ut09 | 495 | 0.70 | 0.70 | 0.66 | 0.62 | 0.64 | 1.17 |
| ut11 | 495 | 0.57 | 0.56 | 0.49 | 0.46 | 0.53 | 1.23 |
| ut12 | 495 | 0.72 | 0.72 | 0.68 | 0.65 | 0.63 | 1.15 |
ut2$item.stats %>%
ggplot(aes(x = row.names(ut2$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Utility. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))kable(ut2$alpha.drop,
caption = "Utility. Subscale statistics when item drop",
label = 17, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| ut01 | 0.86 | 0.86 | 0.86 | 0.38 | 6.21 | 0.01 | 0.01 | 0.40 |
| ut02 | 0.86 | 0.86 | 0.86 | 0.38 | 6.01 | 0.01 | 0.01 | 0.37 |
| ut03 | 0.88 | 0.88 | 0.88 | 0.42 | 7.30 | 0.01 | 0.01 | 0.43 |
| ut04 | 0.88 | 0.88 | 0.88 | 0.43 | 7.41 | 0.01 | 0.01 | 0.43 |
| ut05 | 0.87 | 0.87 | 0.87 | 0.40 | 6.69 | 0.01 | 0.01 | 0.41 |
| ut06 | 0.86 | 0.86 | 0.87 | 0.39 | 6.30 | 0.01 | 0.01 | 0.41 |
| ut07 | 0.87 | 0.87 | 0.87 | 0.41 | 6.99 | 0.01 | 0.01 | 0.42 |
| ut08 | 0.87 | 0.87 | 0.87 | 0.40 | 6.77 | 0.01 | 0.01 | 0.41 |
| ut09 | 0.87 | 0.87 | 0.87 | 0.40 | 6.64 | 0.01 | 0.02 | 0.41 |
| ut11 | 0.88 | 0.88 | 0.88 | 0.42 | 7.27 | 0.01 | 0.01 | 0.44 |
| ut12 | 0.86 | 0.87 | 0.87 | 0.39 | 6.52 | 0.01 | 0.02 | 0.41 |
kable(ut2$response.freq,
caption = "Utility. Non missing response frequency for each item",
label = 18, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| ut01 | 0.01 | 0.01 | 0.06 | 0.29 | 0.34 | 0.28 | 0 |
| ut02 | 0.01 | 0.02 | 0.09 | 0.38 | 0.30 | 0.20 | 0 |
| ut03 | 0.01 | 0.04 | 0.09 | 0.33 | 0.31 | 0.22 | 0 |
| ut04 | 0.02 | 0.08 | 0.15 | 0.40 | 0.27 | 0.09 | 0 |
| ut05 | 0.03 | 0.06 | 0.20 | 0.35 | 0.23 | 0.12 | 0 |
| ut06 | 0.03 | 0.03 | 0.13 | 0.39 | 0.30 | 0.12 | 0 |
| ut07 | 0.01 | 0.05 | 0.19 | 0.35 | 0.26 | 0.14 | 0 |
| ut08 | 0.01 | 0.03 | 0.11 | 0.36 | 0.34 | 0.15 | 0 |
| ut09 | 0.03 | 0.05 | 0.15 | 0.38 | 0.25 | 0.13 | 0 |
| ut11 | 0.05 | 0.12 | 0.27 | 0.32 | 0.18 | 0.07 | 0 |
| ut12 | 0.02 | 0.07 | 0.15 | 0.38 | 0.26 | 0.12 | 0 |
de2 <- psych::alpha(
taia %>% select(all_of(de_items)),
cumulative = TRUE,
title = "Dependability Factor",
check.keys = FALSE
)kable(de2$total,
caption = "Dependability. Subscale statistics",
label = 23, digits = 2,
col.names = total_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Scale Mean | Total Score SD | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|---|
| 0.82 | 0.82 | 0.84 | 0.31 | 4.49 | 0.01 | 26.19 | 7.05 | 0.32 |
de2$item.stats$mean <- de2$item.stats$mean / 5
kable(de2$item.stats,
caption = "Dependability. Items statistics",
label = 24, digits = 2,
col.names = item_stats_colnames)| Num. of Obs. | Discrimination | Std Cor | Cor Overlap Corrected | Cor if drop | Difficulty | SD | |
|---|---|---|---|---|---|---|---|
| de01 | 495 | 0.58 | 0.59 | 0.53 | 0.47 | 0.52 | 1.10 |
| de02 | 495 | 0.72 | 0.72 | 0.68 | 0.62 | 0.43 | 1.15 |
| de03 | 495 | 0.65 | 0.65 | 0.60 | 0.54 | 0.43 | 1.19 |
| de05 | 495 | 0.62 | 0.62 | 0.59 | 0.50 | 0.71 | 1.16 |
| de06 | 495 | 0.67 | 0.67 | 0.62 | 0.56 | 0.45 | 1.23 |
| de07 | 495 | 0.61 | 0.62 | 0.56 | 0.51 | 0.56 | 1.00 |
| de08 | 495 | 0.70 | 0.71 | 0.67 | 0.61 | 0.53 | 1.06 |
| de09 | 495 | 0.46 | 0.45 | 0.40 | 0.31 | 0.69 | 1.20 |
| de10 | 495 | 0.73 | 0.73 | 0.71 | 0.64 | 0.45 | 1.18 |
| de11 | 495 | 0.41 | 0.40 | 0.28 | 0.25 | 0.46 | 1.20 |
de2$item.stats %>%
ggplot(aes(x = row.names(de2$item.stats))) +
geom_point(aes(y = mean), color = "darkblue", size = 3) +
geom_point(aes(y = raw.r), color = "darkred", size = 2) +
geom_hline(yintercept = 0.1, color = "darkblue") +
geom_hline(yintercept = 0.9, color = "darkblue") +
geom_hline(yintercept = 0.25, color = "darkred") +
geom_hline(yintercept = 0, color = "black") +
labs(x = "Item", y = "Value",
title = "Dependability. Items characteristics",
subtitle = "Difficulty (blue) and Dicrimination (red)") +
theme(plot.title = element_text(hjust = .5),
plot.subtitle = element_text(hjust = .5))kable(de2$alpha.drop,
caption = "Dependability. Subscale statistics when item drop",
label = 25, digits = 2,
col.names = alpha_drop_colnames)| Alpha | Standardized Alpha | Guttman’s Lambda 6 | Average interitem correlation | S/N | Alpha SE | Var(r) | Median interitem correlation | |
|---|---|---|---|---|---|---|---|---|
| de01 | 0.80 | 0.80 | 0.82 | 0.31 | 4.11 | 0.01 | 0.02 | 0.31 |
| de02 | 0.79 | 0.79 | 0.81 | 0.29 | 3.72 | 0.01 | 0.02 | 0.29 |
| de03 | 0.79 | 0.80 | 0.82 | 0.30 | 3.93 | 0.01 | 0.02 | 0.29 |
| de05 | 0.80 | 0.80 | 0.80 | 0.31 | 4.04 | 0.01 | 0.02 | 0.35 |
| de06 | 0.79 | 0.80 | 0.81 | 0.30 | 3.88 | 0.01 | 0.02 | 0.31 |
| de07 | 0.80 | 0.80 | 0.82 | 0.31 | 4.02 | 0.01 | 0.02 | 0.29 |
| de08 | 0.79 | 0.79 | 0.81 | 0.29 | 3.75 | 0.01 | 0.02 | 0.29 |
| de09 | 0.82 | 0.82 | 0.82 | 0.34 | 4.60 | 0.01 | 0.02 | 0.35 |
| de10 | 0.78 | 0.79 | 0.80 | 0.29 | 3.67 | 0.01 | 0.02 | 0.29 |
| de11 | 0.83 | 0.83 | 0.84 | 0.35 | 4.79 | 0.01 | 0.02 | 0.37 |
kable(de2$response.freq,
caption = "Dependability. Non missing response frequency for each item",
label = 26, digits = 2)| 0 | 1 | 2 | 3 | 4 | 5 | miss | |
|---|---|---|---|---|---|---|---|
| de01 | 0.05 | 0.11 | 0.24 | 0.43 | 0.14 | 0.03 | 0 |
| de02 | 0.09 | 0.17 | 0.36 | 0.26 | 0.10 | 0.02 | 0 |
| de03 | 0.10 | 0.17 | 0.35 | 0.27 | 0.09 | 0.03 | 0 |
| de05 | 0.02 | 0.03 | 0.10 | 0.28 | 0.34 | 0.23 | 0 |
| de06 | 0.10 | 0.17 | 0.32 | 0.28 | 0.11 | 0.03 | 0 |
| de07 | 0.02 | 0.06 | 0.27 | 0.42 | 0.20 | 0.03 | 0 |
| de08 | 0.04 | 0.10 | 0.24 | 0.44 | 0.15 | 0.03 | 0 |
| de09 | 0.01 | 0.06 | 0.13 | 0.27 | 0.33 | 0.20 | 0 |
| de10 | 0.10 | 0.15 | 0.30 | 0.34 | 0.10 | 0.02 | 0 |
| de11 | 0.07 | 0.20 | 0.28 | 0.31 | 0.12 | 0.03 | 0 |
omega(taia %>% ungroup() %>% select(all_of(taia_items)),
nfactors=6, p=.05, poly=FALSE,
digits=2, title="Omega", sl=TRUE, plot=TRUE, covar=FALSE)## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.94
## G.6: 0.97
## Omega Hierarchical: 0.6
## Omega H asymptotic: 0.62
## Omega Total 0.96
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* F4* F5* F6* h2 u2 p2
## pr01 0.57 0.34 0.26 0.57 0.43 0.58
## pr02 0.45 0.32 0.68 0.65
## pr03 0.20 0.54 0.39 0.61 0.10
## pr04 0.43 0.24 0.76 0.02
## pr05 0.56 0.45 0.52 0.48 0.60
## pr06 0.43 0.37 0.35 0.65 0.53
## pr07 0.57 0.24 0.25 0.21 0.50 0.50 0.66
## pr08 0.52 0.38 0.23 0.49 0.51 0.54
## pr09 0.41 0.29 0.29 0.71 0.57
## pr10 0.37 0.25 0.23 0.77 0.58
## co01 0.51 0.51 0.54 0.46 0.48
## co02 0.44 0.55 0.49 0.51 0.39
## co03 0.47 0.29 0.37 0.63 0.60
## co04 0.33 0.44 0.38 0.62 0.28
## co05 0.44 0.63 0.60 0.40 0.33
## co06 0.37 0.51 0.40 0.60 0.34
## co07- 0.29 0.17 0.83 0.11
## co08 0.34 -0.37 0.41 0.59 0.02
## co09 0.40 0.60 0.54 0.46 0.29
## co10 0.38 0.45 0.40 0.60 0.36
## ut01 0.36 0.67 0.61 0.39 0.21
## ut02 0.45 0.63 0.64 0.36 0.31
## ut03 0.22 0.32 0.52 0.54 0.46 0.09
## ut04 0.27 0.39 0.24 0.76 0.31
## ut05 0.41 0.44 0.37 0.63 0.45
## ut06 0.46 0.60 0.56 0.44 0.38
## ut07 0.34 0.46 0.34 0.66 0.35
## ut08 0.40 0.47 0.40 0.60 0.39
## ut09 0.42 0.46 0.39 0.61 0.44
## ut10 0.24 0.09 0.91 0.03
## ut11 0.54 0.21 0.39 0.48 0.52 0.61
## ut12 0.46 0.47 0.45 0.55 0.47
## fa01 0.44 0.61 0.60 0.40 0.33
## fa02 0.67 0.54 0.46 0.00
## fa03 -0.37 0.31 0.69 0.12
## fa04 0.35 0.32 0.21 0.40 0.60 0.32
## fa05 0.47 0.60 0.62 0.38 0.36
## fa06 0.58 0.30 0.52 0.48 0.65
## fa07 0.23 0.20 0.47 0.36 0.64 0.14
## fa08 0.61 0.49 0.51 0.00
## fa09 0.64 0.23 0.55 0.45 0.02
## fa10 0.24 0.63 0.48 0.52 0.12
## de01 0.43 0.29 0.71 0.63
## de02 0.57 0.23 0.30 0.49 0.51 0.67
## de03 0.50 0.28 0.38 0.62 0.65
## de04- 0.31 0.39 0.27 0.73 0.37
## de05 0.38 0.41 0.29 0.45 0.55 0.32
## de06 0.58 0.47 0.58 0.42 0.59
## de07 0.49 0.34 0.40 0.60 0.59
## de08 0.54 0.23 0.25 0.43 0.57 0.68
## de09 0.22 0.59 0.43 0.57 0.12
## de10 0.60 0.40 0.56 0.44 0.64
## de11 0.22 0.24 0.32 0.27 0.73 0.18
## un01 0.25 0.72 0.63 0.37 0.10
## un02 0.28 0.79 0.70 0.30 0.11
## un03 0.43 -0.26 0.36 0.64 0.10
## un04 0.25 0.68 0.53 0.47 0.12
## un05 0.28 0.77 0.67 0.33 0.11
## un06 -0.29 0.51 0.32 0.40 0.60 0.02
## un07 0.33 0.59 0.54 0.46 0.20
## un08 0.24 0.73 0.60 0.40 0.10
## un09 0.31 0.61 0.52 0.48 0.18
## un10 0.27 0.65 0.57 0.43 0.13
## un11 0.24 0.72 0.60 0.40 0.10
## un12 0.26 0.68 0.57 0.43 0.12
##
## With eigenvalues of:
## g F1* F2* F3* F4* F5* F6*
## 9.4 4.6 5.6 2.8 2.7 1.6 2.3
##
## general/max 1.68 max/min = 3.6
## mean percent general = 0.32 with sd = 0.22 and cv of 0.68
## Explained Common Variance of the general factor = 0.32
##
## The degrees of freedom are 1705 and the fit is 8.03
## The number of observations was 495 with Chi Square = 3753.55 with prob < 2.4e-155
## The root mean square of the residuals is 0.04
## The df corrected root mean square of the residuals is 0.04
## RMSEA index = 0.049 and the 10 % confidence intervals are 0.047 0.051
## BIC = -6825.22
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 2015 and the fit is 22.88
## The number of observations was 495 with Chi Square = 10774.78 with prob < 0
## The root mean square of the residuals is 0.15
## The df corrected root mean square of the residuals is 0.15
##
## RMSEA index = 0.094 and the 10 % confidence intervals are 0.092 0.096
## BIC = -1727.41
##
## Measures of factor score adequacy
## g F1* F2* F3* F4* F5*
## Correlation of scores with factors 0.81 0.89 0.97 0.86 0.94 0.71
## Multiple R square of scores with factors 0.66 0.79 0.94 0.74 0.88 0.50
## Minimum correlation of factor score estimates 0.32 0.59 0.88 0.47 0.76 0.00
## F6*
## Correlation of scores with factors 0.91
## Multiple R square of scores with factors 0.83
## Minimum correlation of factor score estimates 0.66
##
## Total, General and Subset omega for each subset
## g F1* F2* F3* F4* F5*
## Omega total for total scores and subscales 0.96 0.87 0.91 0.86 0.83 0.81
## Omega general for total scores and subscales 0.60 0.43 0.11 0.45 0.09 0.57
## Omega group for total scores and subscales 0.21 0.44 0.79 0.41 0.74 0.24
## F6*
## Omega total for total scores and subscales 0.51
## Omega general for total scores and subscales 0.14
## Omega group for total scores and subscales 0.37
splitHalf(taia %>% ungroup() %>% select(all_of(taia_items)),
raw=F, brute=F, n.sample=100, covar=F,
check.keys=F, key=NULL, use="pairwise")## Split half reliabilities
## Call: splitHalf(r = taia %>% ungroup() %>% select(all_of(taia_items)),
## raw = F, brute = F, n.sample = 100, covar = F, check.keys = F,
## key = NULL, use = "pairwise")
##
## Maximum split half reliability (lambda 4) = 0.95
## Guttman lambda 6 = 0.96
## Average split half reliability = 0.93
## Guttman lambda 3 (alpha) = 0.93
## Guttman lambda 2 = 0.94
## Minimum split half reliability (beta) = 0.87
## Average interitem r = 0.17 with median = 0.18
## Warning: Guttman has been deprecated. The use of the splitHalf function is
## recommended
## Warning in splitHalf(r): Some items were negatively correlated with total scale
## and were automatically reversed.
## Call: guttman(r = taia %>% ungroup() %>% select(all_of(taia_items)))
##
## Alternative estimates of reliability
##
## Guttman bounds
## L1 = 0.92
## L2 = 0.94
## L3 (alpha) = 0.93
## L4 (max) = 0.97
## L5 = 0.92
## L6 (smc) = 0.96
## TenBerge bounds
## mu0 = 0.93 mu1 = 0.94 mu2 = 0.94 mu3 = 0.94
##
## alpha of first PC = 0.95
## estimated greatest lower bound based upon communalities= 0.97
##
## beta found by splitHalf = 0.85
## $glb
## [1] 0.965817
##
## $communality
## pr01 pr02 pr03 pr04 pr05 pr06 pr07 pr08
## 0.7253391 0.5393523 0.4690491 0.6292470 0.7830601 0.6280428 0.5758103 0.6112839
## pr09 pr10 co01 co02 co03 co04 co05 co06
## 0.5896570 0.3330770 0.5758243 0.6305638 0.4895706 0.4890165 0.7521395 0.5726406
## co07 co08 co09 co10 ut01 ut02 ut03 ut04
## 0.4356987 0.4665793 0.6337356 0.4811587 0.7718201 0.7277348 0.6422830 0.3722353
## ut05 ut06 ut07 ut08 ut09 ut10 ut11 ut12
## 0.5234489 0.7171434 0.5757501 0.5792722 0.7253062 0.3644927 0.6451266 0.5389414
## fa01 fa02 fa03 fa04 fa05 fa06 fa07 fa08
## 0.6943597 0.5928412 0.4376811 0.5197275 0.7565165 0.5844505 0.4786126 0.6061162
## fa09 fa10 de01 de02 de03 de04 de05 de06
## 0.6945327 0.4919291 0.4216417 0.5711187 0.5654506 0.4521109 0.7942688 0.7264322
## de07 de08 de09 de10 de11 un01 un02 un03
## 0.5278337 0.5885239 0.6875419 0.6514868 0.3425354 0.6718202 0.7379031 0.4402195
## un04 un05 un06 un07 un08 un09 un10 un11
## 0.6398019 0.6914535 0.4729219 0.5759967 0.6904648 0.5762576 0.6507091 0.6870705
## un12
## 0.6276157
##
## $numf
## [1] 19
##
## $Call
## glb.fa(r = taia %>% ungroup() %>% select(all_of(taia_items)))
6 factors, varimax rotation
efa_6f_vm <- factanal(taia %>% select(all_of(taia_items)),
factors = 6,
scores = "regression",
rotation = "varimax")##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## pr01 0.489 0.475 0.172 0.287
## pr02 0.263 0.392 0.219 0.164 0.181
## pr03 0.219 0.547
## pr04 -0.109 0.139 0.437
## pr05 0.204 0.350 0.139 0.123 0.676
## pr06 0.475 0.279 0.144 0.105
## pr07 0.387 0.525 0.208 0.134
## pr08 0.492 0.429 0.125 0.241
## pr09 0.350 0.347 0.133 0.220
## pr10 0.328 0.301 0.124 0.108
## co01 0.243 0.676 0.116
## co02 0.169 0.626 0.115
## co03 0.416 0.370 0.159 0.141
## co04 0.532 0.147 0.121 0.161
## co05 0.137 0.724
## co06 0.152 0.554 -0.117
## co08 -0.118 0.413 -0.115 -0.127 -0.467
## co09 0.676 -0.160
## co10 0.184 0.526 0.206 -0.123
## ut01 0.810
## ut02 0.806 0.102 0.135 0.125
## ut03 0.472 -0.102 0.119 0.507
## ut04 0.461 0.104 0.132
## ut05 0.598 0.194
## ut06 0.717 0.164 0.125
## ut07 0.550 0.207
## ut08 0.576 0.249
## ut09 0.593 0.174 0.102 0.128
## ut11 0.368 0.255 0.158 0.126 0.591
## ut12 0.604 0.233 0.135 0.142
## fa01 0.300 0.345 0.628
## fa02 -0.207 0.669 0.148
## fa03 -0.116 0.385 -0.318 0.148
## fa04 0.549 0.103 -0.192 0.177
## fa05 0.293 0.411 0.622
## fa06 0.301 0.573 0.126 0.188 0.182 0.163
## fa07 0.196 0.474 0.193
## fa08 -0.191 0.602 0.232
## fa09 -0.155 0.631 0.310
## fa10 0.280 0.136 0.599
## de01 0.278 0.429 0.162
## de02 0.225 0.571 0.129 0.155 0.228
## de03 0.223 0.475 0.115 0.138 0.214
## de05 0.508 0.173 0.137 0.385
## de06 0.218 0.352 0.184 0.171 0.666
## de07 0.439 0.301 0.220 0.182
## de08 0.345 0.412 0.193 0.107 0.185 0.205
## de09 0.259 0.592
## de10 0.287 0.512 0.177 0.351
## de11 0.181 0.332 0.255
## un01 0.287 0.731
## un02 0.138 0.815
## un03 0.207 0.502 -0.247
## un04 0.113 0.711
## un05 0.191 0.791
## un06 -0.131 0.518 0.189
## un07 0.286 0.648 -0.139 0.128
## un08 0.182 0.746
## un09 0.162 0.662 0.197
## un10 0.261 0.690
## un11 0.763
## un12 0.236 0.713
##
## Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## SS loadings 7.605 7.077 6.534 2.832 2.786 2.029
## Proportion Var 0.123 0.114 0.105 0.046 0.045 0.033
## Cumulative Var 0.123 0.237 0.342 0.388 0.433 0.466
| U | |
|---|---|
| de11 | 0.7817421 |
| pr04 | 0.7700534 |
| pr10 | 0.7616189 |
| ut04 | 0.7559106 |
| fa03 | 0.7134353 |
| de01 | 0.7002496 |
| fa07 | 0.6907293 |
| pr09 | 0.6888357 |
| un06 | 0.6751271 |
| pr02 | 0.6694849 |
| pr06 | 0.6576002 |
| ut07 | 0.6510578 |
| co04 | 0.6481729 |
| co06 | 0.6474121 |
| de03 | 0.6464066 |
| un03 | 0.6402866 |
| pr03 | 0.6384384 |
| co03 | 0.6327467 |
| co10 | 0.6282188 |
| de07 | 0.6246089 |
| fa04 | 0.6137315 |
| ut08 | 0.5910384 |
| ut05 | 0.5868681 |
| ut09 | 0.5865585 |
| de08 | 0.5858475 |
| de09 | 0.5766570 |
| co08 | 0.5671255 |
| co02 | 0.5579891 |
| de05 | 0.5437340 |
| fa10 | 0.5420869 |
| ut12 | 0.5382611 |
| fa08 | 0.5284220 |
| de02 | 0.5246474 |
| co09 | 0.5063667 |
| pr07 | 0.4996945 |
| pr08 | 0.4983024 |
| ut03 | 0.4937529 |
| de10 | 0.4934491 |
| un09 | 0.4875879 |
| fa09 | 0.4737183 |
| fa02 | 0.4731288 |
| fa06 | 0.4702934 |
| un04 | 0.4642368 |
| co01 | 0.4635405 |
| un07 | 0.4574466 |
| co05 | 0.4479825 |
| ut06 | 0.4362455 |
| un10 | 0.4358851 |
| un12 | 0.4312571 |
| pr01 | 0.4144342 |
| ut11 | 0.4071750 |
| un08 | 0.4066783 |
| un11 | 0.3931924 |
| fa01 | 0.3816574 |
| un01 | 0.3730108 |
| fa05 | 0.3486393 |
| pr05 | 0.3357658 |
| ut01 | 0.3321338 |
| un05 | 0.3254634 |
| de06 | 0.3122163 |
| un02 | 0.3085120 |
| ut02 | 0.3006345 |
6 factors, promax rotation
efa_6f_pm <- factanal(taia %>% select(all_of(taia_items)),
factors = 6,
scores = "regression",
rotation = "promax")##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## pr01 0.461 0.225 0.283
## pr02 0.338 0.110 0.148 0.136
## pr03 -0.110 0.614
## pr04 -0.141 0.484
## pr05 0.790
## pr06 0.174 0.410
## pr07 0.527 0.131 0.192
## pr08 0.422 0.278 0.238
## pr09 0.361 0.146 0.227
## pr10 0.263 0.204 -0.122
## co01 0.809 0.102 -0.145
## co02 0.765 -0.137
## co03 0.303 0.264 0.114
## co04 0.493 0.121 -0.132
## co05 0.902 -0.102 -0.117 -0.109
## co06 0.668 -0.154
## co08 0.542 -0.180 -0.501 -0.100
## co09 0.817 -0.113 -0.123 -0.169
## co10 0.598 0.115 -0.135
## ut01 -0.249 0.974
## ut02 -0.231 0.910 0.113
## ut03 -0.213 0.385 0.515
## ut04 -0.198 0.552 0.146
## ut05 0.658 -0.124
## ut06 0.789 0.108
## ut07 0.128 0.593
## ut08 0.186 0.543 -0.108
## ut09 0.608 0.110
## ut11 0.291 0.694
## ut12 0.106 0.559
## fa01 0.277 0.125 0.171 0.650
## fa02 -0.129 0.695
## fa03 0.418 -0.154 -0.379 0.158
## fa04 0.609 -0.225 -0.241 0.146
## fa05 0.367 0.105 0.156 -0.119 0.648
## fa06 0.580 0.149 0.131
## fa07 -0.218 0.508 0.109 0.173
## fa08 -0.179 0.165 0.604
## fa09 -0.187 0.254 0.627
## fa10 0.281 -0.127 0.648 -0.119
## de01 0.460
## de02 0.569 0.135 0.166
## de03 0.430 0.173
## de05 0.114 0.340 0.407 -0.107
## de06 0.770
## de07 0.160 0.109 0.337 0.156
## de08 0.329 0.129 0.141 0.153
## de09 0.692 -0.101
## de10 0.397 0.106 0.103 0.339
## de11 -0.106 -0.103 0.320 0.101 0.273
## un01 -0.148 0.804 0.225 0.123 -0.141
## un02 0.857 -0.105
## un03 0.451 0.145 -0.247
## un04 0.734
## un05 0.844
## un06 0.556 -0.347 0.256
## un07 0.192 0.641 -0.115 -0.172 0.113
## un08 -0.114 0.817 0.113
## un09 0.646 -0.101 0.212
## un10 0.219 0.698 -0.212
## un11 0.803 -0.103
## un12 -0.180 0.740 0.166
##
## Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## SS loadings 7.535 6.547 6.389 3.041 2.823 2.419
## Proportion Var 0.122 0.106 0.103 0.049 0.046 0.039
## Cumulative Var 0.122 0.227 0.330 0.379 0.425 0.464
| U | |
|---|---|
| de11 | 0.7817421 |
| pr04 | 0.7700534 |
| pr10 | 0.7616189 |
| ut04 | 0.7559106 |
| fa03 | 0.7134353 |
| de01 | 0.7002496 |
| fa07 | 0.6907293 |
| pr09 | 0.6888357 |
| un06 | 0.6751271 |
| pr02 | 0.6694849 |
| pr06 | 0.6576002 |
| ut07 | 0.6510578 |
| co04 | 0.6481729 |
| co06 | 0.6474121 |
| de03 | 0.6464066 |
| un03 | 0.6402866 |
| pr03 | 0.6384384 |
| co03 | 0.6327467 |
| co10 | 0.6282188 |
| de07 | 0.6246089 |
| fa04 | 0.6137315 |
| ut08 | 0.5910384 |
| ut05 | 0.5868681 |
| ut09 | 0.5865585 |
| de08 | 0.5858475 |
| de09 | 0.5766570 |
| co08 | 0.5671255 |
| co02 | 0.5579891 |
| de05 | 0.5437340 |
| fa10 | 0.5420869 |
| ut12 | 0.5382611 |
| fa08 | 0.5284220 |
| de02 | 0.5246474 |
| co09 | 0.5063667 |
| pr07 | 0.4996945 |
| pr08 | 0.4983024 |
| ut03 | 0.4937529 |
| de10 | 0.4934491 |
| un09 | 0.4875879 |
| fa09 | 0.4737183 |
| fa02 | 0.4731288 |
| fa06 | 0.4702934 |
| un04 | 0.4642368 |
| co01 | 0.4635405 |
| un07 | 0.4574466 |
| co05 | 0.4479825 |
| ut06 | 0.4362455 |
| un10 | 0.4358851 |
| un12 | 0.4312571 |
| pr01 | 0.4144342 |
| ut11 | 0.4071750 |
| un08 | 0.4066783 |
| un11 | 0.3931924 |
| fa01 | 0.3816574 |
| un01 | 0.3730108 |
| fa05 | 0.3486393 |
| pr05 | 0.3357658 |
| ut01 | 0.3321338 |
| un05 | 0.3254634 |
| de06 | 0.3122163 |
| un02 | 0.3085120 |
| ut02 | 0.3006345 |
5 factors, varimax rotation
efa_5f_vm <- factanal(taia %>% select(all_of(taia_items)),
factors = 5,
scores = "regression",
rotation = "varimax")##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5
## pr01 0.576 0.372 0.170 0.212
## pr02 0.319 0.359 0.221 0.205
## pr03 0.306 -0.108 0.412 0.121
## pr04 0.131 -0.197 0.340 0.154
## pr05 0.211 0.453 0.156 0.482
## pr06 0.501 0.249 0.142 0.100
## pr07 0.456 0.465 0.232
## pr08 0.567 0.334 0.123 0.160
## pr09 0.424 0.257 0.129 0.129
## pr10 0.369 0.262 0.124 0.104
## co01 0.303 0.625 0.130
## co02 0.235 0.573 0.114 0.112
## co03 0.462 0.329 0.176
## co04 0.573 0.115
## co05 0.197 0.690
## co06 0.207 0.520
## co08 -0.157 0.505 -0.112 -0.326 -0.146
## co09 0.121 0.681 -0.100
## co10 0.219 0.513 0.206 -0.125
## ut01 0.788
## ut02 0.792 0.100 0.136
## ut03 0.531 -0.225 0.358 0.140
## ut04 0.437 0.115
## ut05 0.586 0.191
## ut06 0.713 0.148
## ut07 0.559 0.171
## ut08 0.612 0.179
## ut09 0.601 0.152 0.128
## ut11 0.362 0.341 0.171 0.431
## ut12 0.632 0.179 0.131 0.115
## fa01 0.317 0.338 0.123 0.618
## fa02 -0.206 0.202 0.659
## fa03 -0.148 0.484 -0.103
## fa04 0.616
## fa05 0.305 0.412 0.604
## fa06 0.366 0.528 0.127 0.241 0.179
## fa07 0.512 0.182
## fa08 -0.190 0.284 0.591
## fa09 -0.155 0.321 0.632
## fa10 0.278 0.126 0.611
## de01 0.320 0.386 0.159
## de02 0.282 0.553 0.132 0.281
## de03 0.246 0.493 0.117 0.176 0.105
## de05 0.583 0.133 0.230
## de06 0.219 0.462 0.197 0.470
## de07 0.462 0.290 0.220 0.182
## de08 0.392 0.386 0.195 0.273
## de09 0.367 0.374
## de10 0.300 0.558 0.303 0.121
## de11 0.445 0.153
## un01 0.301 0.727
## un02 0.124 0.815
## un03 0.221 0.501 -0.243
## un04 0.107 0.116 0.712
## un05 0.219 0.787
## un06 0.519 0.148
## un07 0.328 0.651
## un08 0.195 0.744
## un09 0.198 0.667 0.124 -0.110
## un10 0.272 0.692 -0.101
## un11 0.107 0.764
## un12 0.242 0.712
##
## Factor1 Factor2 Factor3 Factor4 Factor5
## SS loadings 8.695 6.882 6.542 2.836 2.744
## Proportion Var 0.140 0.111 0.106 0.046 0.044
## Cumulative Var 0.140 0.251 0.357 0.403 0.447
| U | |
|---|---|
| pr04 | 0.8036037 |
| ut04 | 0.7875168 |
| de11 | 0.7660851 |
| pr10 | 0.7648954 |
| fa03 | 0.7322846 |
| pr09 | 0.7157810 |
| de09 | 0.7138430 |
| de01 | 0.7115636 |
| pr03 | 0.7071547 |
| un06 | 0.6984492 |
| fa07 | 0.6934404 |
| pr02 | 0.6780781 |
| co06 | 0.6691958 |
| pr06 | 0.6557484 |
| ut07 | 0.6500545 |
| co04 | 0.6435044 |
| de03 | 0.6411034 |
| un03 | 0.6404520 |
| co03 | 0.6336224 |
| co10 | 0.6291329 |
| de07 | 0.6207641 |
| ut05 | 0.6108805 |
| fa04 | 0.6096628 |
| ut09 | 0.5950296 |
| co02 | 0.5895055 |
| ut08 | 0.5880908 |
| de05 | 0.5859234 |
| co08 | 0.5800904 |
| de08 | 0.5776240 |
| ut12 | 0.5381209 |
| ut11 | 0.5378917 |
| fa10 | 0.5312032 |
| fa08 | 0.5256412 |
| pr08 | 0.5255447 |
| ut03 | 0.5181711 |
| de02 | 0.5158859 |
| co09 | 0.5114014 |
| pr07 | 0.5096638 |
| co01 | 0.4937745 |
| pr05 | 0.4926627 |
| de10 | 0.4869821 |
| un09 | 0.4861097 |
| fa06 | 0.4806029 |
| co05 | 0.4747549 |
| fa09 | 0.4734086 |
| fa02 | 0.4713133 |
| de06 | 0.4691654 |
| un04 | 0.4646410 |
| un07 | 0.4595727 |
| ut06 | 0.4594009 |
| pr01 | 0.4496805 |
| un10 | 0.4361787 |
| un12 | 0.4325425 |
| un08 | 0.4008023 |
| un11 | 0.3971241 |
| fa01 | 0.3854741 |
| ut01 | 0.3691461 |
| un01 | 0.3671937 |
| fa05 | 0.3613714 |
| ut02 | 0.3407660 |
| un05 | 0.3303704 |
| un02 | 0.3106689 |
5 factors, promax rotation
efa_5f_pm <- factanal(taia %>% select(all_of(taia_items)),
factors = 5,
scores = "regression",
rotation = "promax")##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5
## pr01 0.446 0.279 0.163
## pr02 0.143 0.296 0.114 0.211
## pr03 0.233 -0.214 -0.117 0.424
## pr04 -0.293 0.354 0.107
## pr05 -0.115 0.392 0.579
## pr06 0.456 0.170
## pr07 0.281 0.412 0.218
## pr08 0.480 0.250 0.106
## pr09 0.342 0.189
## pr10 0.300 0.206 -0.112
## co01 0.146 0.650 0.122
## co02 0.599 0.111
## co03 0.354 0.265 0.139
## co04 0.624
## co05 0.759 -0.112
## co06 0.545 -0.122
## co08 -0.181 0.655 -0.178 -0.313 -0.109
## co09 0.763 -0.105 -0.100
## co10 0.114 0.531 0.122 -0.157
## ut01 0.932 -0.173 -0.161
## ut02 0.859 -0.129
## ut03 0.556 -0.375 0.303
## ut04 0.452
## ut05 0.610 0.111
## ut06 0.757
## ut07 0.631 0.114 -0.191
## ut08 0.655 -0.102
## ut09 0.605
## ut11 0.121 0.247 0.486
## ut12 0.625
## fa01 0.152 0.310 0.125 0.634
## fa02 -0.203 0.143 0.681
## fa03 -0.291 0.581
## fa04 -0.281 0.695
## fa05 0.129 0.400 0.620
## fa06 0.134 0.490 0.229 0.130
## fa07 -0.104 0.583 0.102
## fa08 -0.210 0.254 0.592
## fa09 -0.158 0.280 0.627
## fa10 0.247 0.110 -0.175 0.648
## de01 0.200 0.356
## de02 0.524 0.319
## de03 0.478 0.183
## de05 0.562 0.179
## de06 -0.118 0.396 0.550
## de07 0.346 0.199 0.104 0.156
## de08 0.192 0.310 0.272
## de09 0.305 -0.213 0.381
## de10 0.534 0.332
## de11 -0.110 0.509
## un01 0.258 -0.197 0.810 -0.169 0.132
## un02 0.857
## un03 0.185 0.450 -0.246
## un04 0.739
## un05 0.103 -0.121 0.847
## un06 -0.250 -0.119 0.556 0.200
## un07 -0.175 0.266 0.641
## un08 0.101 -0.128 0.827 -0.111 0.126
## un09 -0.166 0.647 0.170
## un10 -0.202 0.204 0.699
## un11 0.805
## un12 0.151 -0.155 0.746
##
## Factor1 Factor2 Factor3 Factor4 Factor5
## SS loadings 7.635 6.936 6.600 3.174 2.713
## Proportion Var 0.123 0.112 0.106 0.051 0.044
## Cumulative Var 0.123 0.235 0.341 0.393 0.436
| U | |
|---|---|
| pr04 | 0.8036037 |
| ut04 | 0.7875168 |
| de11 | 0.7660851 |
| pr10 | 0.7648954 |
| fa03 | 0.7322846 |
| pr09 | 0.7157810 |
| de09 | 0.7138430 |
| de01 | 0.7115636 |
| pr03 | 0.7071547 |
| un06 | 0.6984492 |
| fa07 | 0.6934404 |
| pr02 | 0.6780781 |
| co06 | 0.6691958 |
| pr06 | 0.6557484 |
| ut07 | 0.6500545 |
| co04 | 0.6435044 |
| de03 | 0.6411034 |
| un03 | 0.6404520 |
| co03 | 0.6336224 |
| co10 | 0.6291329 |
| de07 | 0.6207641 |
| ut05 | 0.6108805 |
| fa04 | 0.6096628 |
| ut09 | 0.5950296 |
| co02 | 0.5895055 |
| ut08 | 0.5880908 |
| de05 | 0.5859234 |
| co08 | 0.5800904 |
| de08 | 0.5776240 |
| ut12 | 0.5381209 |
| ut11 | 0.5378917 |
| fa10 | 0.5312032 |
| fa08 | 0.5256412 |
| pr08 | 0.5255447 |
| ut03 | 0.5181711 |
| de02 | 0.5158859 |
| co09 | 0.5114014 |
| pr07 | 0.5096638 |
| co01 | 0.4937745 |
| pr05 | 0.4926627 |
| de10 | 0.4869821 |
| un09 | 0.4861097 |
| fa06 | 0.4806029 |
| co05 | 0.4747549 |
| fa09 | 0.4734086 |
| fa02 | 0.4713133 |
| de06 | 0.4691654 |
| un04 | 0.4646410 |
| un07 | 0.4595727 |
| ut06 | 0.4594009 |
| pr01 | 0.4496805 |
| un10 | 0.4361787 |
| un12 | 0.4325425 |
| un08 | 0.4008023 |
| un11 | 0.3971241 |
| fa01 | 0.3854741 |
| ut01 | 0.3691461 |
| un01 | 0.3671937 |
| fa05 | 0.3613714 |
| ut02 | 0.3407660 |
| un05 | 0.3303704 |
| un02 | 0.3106689 |
Model:
mdl1 <- "
PR =~ pr01 + pr02 + pr03 + pr04 + pr05 + pr06 + pr07 + pr08 + pr09 + pr10
CO =~ co01 + co02 + co03 + co04 + co05 + co06 + co08 + co09 + co10
UT =~ ut01 + ut02 + ut03 + ut04 + ut05 + ut06 + ut07 + ut08 + ut09 + ut11 + ut12
FA =~ fa01 + fa02 + fa03 + fa04 + fa05 + fa06 + fa07 + fa08 + fa09 + fa10
DE =~ de01 + de02 + de03 + de05 + de06 + de07 + de08 + de09 + de10 + de11
UN =~ un01 + un02 + un03 + un04 + un05 + un06 + un07 + un08 + un09 + un10 + un11 + un12
"CFA model fitting:
## lavaan 0.6-8 ended normally after 62 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 139
##
## Number of observations 495
##
## Model Test User Model:
##
## Test statistic 6326.223
## Degrees of freedom 1814
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PR =~
## pr01 1.000
## pr02 0.727 0.055 13.199 0.000
## pr03 0.386 0.060 6.384 0.000
## pr04 0.153 0.063 2.431 0.015
## pr05 0.870 0.068 12.721 0.000
## pr06 0.808 0.061 13.285 0.000
## pr07 1.040 0.061 17.095 0.000
## pr08 0.847 0.050 17.029 0.000
## pr09 0.714 0.054 13.167 0.000
## pr10 0.666 0.060 11.105 0.000
## CO =~
## co01 1.000
## co02 0.896 0.061 14.596 0.000
## co03 0.646 0.061 10.618 0.000
## co04 0.459 0.065 7.065 0.000
## co05 1.090 0.065 16.652 0.000
## co06 0.856 0.066 13.052 0.000
## co08 0.438 0.062 7.047 0.000
## co09 0.978 0.063 15.527 0.000
## co10 0.831 0.065 12.804 0.000
## UT =~
## ut01 1.000
## ut02 1.083 0.055 19.815 0.000
## ut03 0.682 0.062 11.048 0.000
## ut04 0.636 0.062 10.244 0.000
## ut05 0.965 0.066 14.724 0.000
## ut06 1.008 0.058 17.348 0.000
## ut07 0.790 0.062 12.676 0.000
## ut08 0.807 0.057 14.082 0.000
## ut09 0.945 0.063 14.930 0.000
## ut11 0.789 0.068 11.527 0.000
## ut12 0.974 0.062 15.791 0.000
## FA =~
## fa01 1.000
## fa02 0.535 0.060 8.869 0.000
## fa03 0.219 0.060 3.670 0.000
## fa04 0.424 0.056 7.575 0.000
## fa05 1.028 0.051 20.280 0.000
## fa06 0.716 0.053 13.558 0.000
## fa07 0.335 0.057 5.911 0.000
## fa08 0.487 0.058 8.366 0.000
## fa09 0.626 0.059 10.519 0.000
## fa10 0.785 0.059 13.357 0.000
## DE =~
## de01 1.000
## de02 1.315 0.113 11.688 0.000
## de03 1.183 0.111 10.668 0.000
## de05 0.981 0.103 9.509 0.000
## de06 1.276 0.116 11.019 0.000
## de07 0.979 0.093 10.586 0.000
## de08 1.185 0.102 11.579 0.000
## de09 0.604 0.098 6.189 0.000
## de10 1.377 0.116 11.853 0.000
## de11 0.476 0.096 4.958 0.000
## UN =~
## un01 1.000
## un02 1.215 0.064 18.860 0.000
## un03 0.817 0.068 11.959 0.000
## un04 1.025 0.062 16.485 0.000
## un05 1.142 0.063 18.233 0.000
## un06 0.783 0.072 10.830 0.000
## un07 1.040 0.068 15.336 0.000
## un08 1.120 0.066 16.897 0.000
## un09 1.090 0.071 15.404 0.000
## un10 1.050 0.066 15.883 0.000
## un11 1.192 0.069 17.382 0.000
## un12 1.065 0.064 16.524 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## PR ~~
## CO 0.417 0.043 9.702 0.000
## UT 0.479 0.045 10.634 0.000
## FA 0.402 0.044 9.066 0.000
## DE 0.423 0.045 9.495 0.000
## UN 0.232 0.034 6.779 0.000
## CO ~~
## UT 0.280 0.038 7.332 0.000
## FA 0.355 0.044 8.018 0.000
## DE 0.326 0.039 8.350 0.000
## UN 0.171 0.033 5.125 0.000
## UT ~~
## FA 0.331 0.043 7.706 0.000
## DE 0.341 0.040 8.621 0.000
## UN 0.187 0.034 5.560 0.000
## FA ~~
## DE 0.366 0.043 8.534 0.000
## UN 0.061 0.035 1.738 0.082
## DE ~~
## UN 0.186 0.029 6.327 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .pr01 0.366 0.028 13.060 0.000
## .pr02 0.619 0.041 14.916 0.000
## .pr03 0.936 0.060 15.582 0.000
## .pr04 1.071 0.068 15.711 0.000
## .pr05 0.977 0.065 14.993 0.000
## .pr06 0.751 0.050 14.902 0.000
## .pr07 0.581 0.042 13.928 0.000
## .pr08 0.390 0.028 13.953 0.000
## .pr09 0.601 0.040 14.921 0.000
## .pr10 0.805 0.053 15.208 0.000
## .co01 0.526 0.040 13.135 0.000
## .co02 0.573 0.041 13.824 0.000
## .co03 0.780 0.052 15.010 0.000
## .co04 1.045 0.068 15.460 0.000
## .co05 0.481 0.039 12.355 0.000
## .co06 0.763 0.053 14.429 0.000
## .co08 0.957 0.062 15.462 0.000
## .co09 0.536 0.040 13.296 0.000
## .co10 0.763 0.053 14.505 0.000
## .ut01 0.443 0.033 13.514 0.000
## .ut02 0.333 0.027 12.266 0.000
## .ut03 0.918 0.060 15.239 0.000
## .ut04 0.959 0.063 15.322 0.000
## .ut05 0.844 0.058 14.654 0.000
## .ut06 0.528 0.038 13.844 0.000
## .ut07 0.866 0.058 15.029 0.000
## .ut08 0.674 0.046 14.788 0.000
## .ut09 0.776 0.053 14.606 0.000
## .ut11 1.107 0.073 15.183 0.000
## .ut12 0.689 0.048 14.384 0.000
## .fa01 0.393 0.036 11.016 0.000
## .fa02 1.156 0.076 15.296 0.000
## .fa03 1.245 0.079 15.664 0.000
## .fa04 1.028 0.067 15.424 0.000
## .fa05 0.348 0.034 10.153 0.000
## .fa06 0.744 0.051 14.506 0.000
## .fa07 1.092 0.070 15.551 0.000
## .fa08 1.094 0.071 15.349 0.000
## .fa09 1.071 0.071 15.086 0.000
## .fa10 0.932 0.064 14.555 0.000
## .de01 0.822 0.055 14.942 0.000
## .de02 0.678 0.048 14.054 0.000
## .de03 0.895 0.061 14.713 0.000
## .de05 0.985 0.065 15.098 0.000
## .de06 0.891 0.061 14.538 0.000
## .de07 0.636 0.043 14.749 0.000
## .de08 0.583 0.041 14.151 0.000
## .de09 1.297 0.083 15.551 0.000
## .de10 0.678 0.049 13.889 0.000
## .de11 1.361 0.087 15.625 0.000
## .un01 0.500 0.035 14.388 0.000
## .un02 0.399 0.030 13.239 0.000
## .un03 0.962 0.063 15.267 0.000
## .un04 0.541 0.037 14.425 0.000
## .un05 0.425 0.031 13.665 0.000
## .un06 1.147 0.075 15.374 0.000
## .un07 0.729 0.049 14.737 0.000
## .un08 0.583 0.041 14.285 0.000
## .un09 0.789 0.054 14.721 0.000
## .un10 0.655 0.045 14.601 0.000
## .un11 0.583 0.041 14.094 0.000
## .un12 0.578 0.040 14.413 0.000
## PR 0.605 0.059 10.207 0.000
## CO 0.633 0.069 9.144 0.000
## UT 0.659 0.066 9.942 0.000
## FA 0.816 0.077 10.599 0.000
## DE 0.377 0.058 6.490 0.000
## UN 0.604 0.064 9.418 0.000
Fit measures:
kable(tibble(
`Model 1` = c(
"Chi-Squared",
"DF",
"p",
"GFI",
"AGFI",
"CFI",
"TLI",
"SRMR",
"RMSEA"
),
Value = round(fitmeasures(
model1,
c(
"chisq",
"df",
"pvalue",
"gfi",
"agfi",
"cfi",
"tli",
"srmr",
"rmsea"
)
), 4)
))| Model 1 | Value |
|---|---|
| Chi-Squared | 6326.2235 |
| DF | 1814.0000 |
| p | 0.0000 |
| GFI | 0.6381 |
| AGFI | 0.6104 |
| CFI | 0.7096 |
| TLI | 0.6973 |
| SRMR | 0.1010 |
| RMSEA | 0.0709 |
Standardized solution:
Loadings:
kable(
smodel1 %>%
filter(op == "=~"),
col.names = c(
"Factor",
"",
"Item",
"Loading",
"SE",
"z",
"p",
"CI lower bound",
"CI upper bound"
),
digits = 3
)| Factor | Item | Loading | SE | z | p | CI lower bound | CI upper bound | |
|---|---|---|---|---|---|---|---|---|
| PR | =~ | pr01 | 0.789 | 0.020 | 39.913 | 0.000 | 0.751 | 0.828 |
| PR | =~ | pr02 | 0.584 | 0.032 | 18.261 | 0.000 | 0.521 | 0.647 |
| PR | =~ | pr03 | 0.296 | 0.043 | 6.854 | 0.000 | 0.211 | 0.381 |
| PR | =~ | pr04 | 0.114 | 0.047 | 2.456 | 0.014 | 0.023 | 0.206 |
| PR | =~ | pr05 | 0.565 | 0.033 | 17.164 | 0.000 | 0.501 | 0.630 |
| PR | =~ | pr06 | 0.587 | 0.032 | 18.467 | 0.000 | 0.525 | 0.650 |
| PR | =~ | pr07 | 0.728 | 0.024 | 30.723 | 0.000 | 0.682 | 0.775 |
| PR | =~ | pr08 | 0.726 | 0.024 | 30.439 | 0.000 | 0.679 | 0.773 |
| PR | =~ | pr09 | 0.583 | 0.032 | 18.186 | 0.000 | 0.520 | 0.645 |
| PR | =~ | pr10 | 0.500 | 0.036 | 13.888 | 0.000 | 0.429 | 0.570 |
| CO | =~ | co01 | 0.739 | 0.024 | 30.666 | 0.000 | 0.692 | 0.786 |
| CO | =~ | co02 | 0.686 | 0.027 | 25.127 | 0.000 | 0.632 | 0.739 |
| CO | =~ | co03 | 0.503 | 0.037 | 13.698 | 0.000 | 0.431 | 0.575 |
| CO | =~ | co04 | 0.337 | 0.043 | 7.853 | 0.000 | 0.253 | 0.421 |
| CO | =~ | co05 | 0.781 | 0.022 | 36.274 | 0.000 | 0.739 | 0.823 |
| CO | =~ | co06 | 0.615 | 0.031 | 19.688 | 0.000 | 0.554 | 0.676 |
| CO | =~ | co08 | 0.336 | 0.043 | 7.829 | 0.000 | 0.252 | 0.420 |
| CO | =~ | co09 | 0.728 | 0.025 | 29.429 | 0.000 | 0.680 | 0.777 |
| CO | =~ | co10 | 0.604 | 0.032 | 18.958 | 0.000 | 0.541 | 0.666 |
| UT | =~ | ut01 | 0.773 | 0.021 | 37.597 | 0.000 | 0.733 | 0.814 |
| UT | =~ | ut02 | 0.836 | 0.016 | 51.142 | 0.000 | 0.804 | 0.868 |
| UT | =~ | ut03 | 0.500 | 0.036 | 13.954 | 0.000 | 0.430 | 0.570 |
| UT | =~ | ut04 | 0.466 | 0.037 | 12.493 | 0.000 | 0.393 | 0.539 |
| UT | =~ | ut05 | 0.649 | 0.028 | 22.945 | 0.000 | 0.593 | 0.704 |
| UT | =~ | ut06 | 0.748 | 0.022 | 33.612 | 0.000 | 0.704 | 0.791 |
| UT | =~ | ut07 | 0.568 | 0.033 | 17.378 | 0.000 | 0.504 | 0.632 |
| UT | =~ | ut08 | 0.624 | 0.030 | 21.010 | 0.000 | 0.566 | 0.682 |
| UT | =~ | ut09 | 0.657 | 0.028 | 23.611 | 0.000 | 0.602 | 0.711 |
| UT | =~ | ut11 | 0.520 | 0.035 | 14.890 | 0.000 | 0.452 | 0.589 |
| UT | =~ | ut12 | 0.690 | 0.026 | 26.662 | 0.000 | 0.639 | 0.741 |
| FA | =~ | fa01 | 0.822 | 0.019 | 42.299 | 0.000 | 0.784 | 0.860 |
| FA | =~ | fa02 | 0.410 | 0.040 | 10.123 | 0.000 | 0.330 | 0.489 |
| FA | =~ | fa03 | 0.175 | 0.047 | 3.752 | 0.000 | 0.083 | 0.266 |
| FA | =~ | fa04 | 0.353 | 0.042 | 8.337 | 0.000 | 0.270 | 0.436 |
| FA | =~ | fa05 | 0.844 | 0.018 | 46.444 | 0.000 | 0.809 | 0.880 |
| FA | =~ | fa06 | 0.600 | 0.032 | 18.680 | 0.000 | 0.537 | 0.663 |
| FA | =~ | fa07 | 0.279 | 0.044 | 6.264 | 0.000 | 0.191 | 0.366 |
| FA | =~ | fa08 | 0.388 | 0.041 | 9.407 | 0.000 | 0.307 | 0.469 |
| FA | =~ | fa09 | 0.479 | 0.038 | 12.693 | 0.000 | 0.405 | 0.553 |
| FA | =~ | fa10 | 0.592 | 0.033 | 18.218 | 0.000 | 0.528 | 0.656 |
| DE | =~ | de01 | 0.561 | 0.033 | 16.808 | 0.000 | 0.495 | 0.626 |
| DE | =~ | de02 | 0.700 | 0.026 | 27.265 | 0.000 | 0.650 | 0.751 |
| DE | =~ | de03 | 0.609 | 0.031 | 19.717 | 0.000 | 0.548 | 0.670 |
| DE | =~ | de05 | 0.519 | 0.035 | 14.677 | 0.000 | 0.450 | 0.588 |
| DE | =~ | de06 | 0.639 | 0.029 | 21.837 | 0.000 | 0.582 | 0.696 |
| DE | =~ | de07 | 0.602 | 0.031 | 19.273 | 0.000 | 0.541 | 0.663 |
| DE | =~ | de08 | 0.690 | 0.026 | 26.215 | 0.000 | 0.638 | 0.741 |
| DE | =~ | de09 | 0.310 | 0.043 | 7.191 | 0.000 | 0.225 | 0.394 |
| DE | =~ | de10 | 0.716 | 0.025 | 29.002 | 0.000 | 0.668 | 0.765 |
| DE | =~ | de11 | 0.243 | 0.045 | 5.436 | 0.000 | 0.155 | 0.331 |
| UN | =~ | un01 | 0.740 | 0.022 | 33.462 | 0.000 | 0.696 | 0.783 |
| UN | =~ | un02 | 0.831 | 0.016 | 52.491 | 0.000 | 0.800 | 0.862 |
| UN | =~ | un03 | 0.544 | 0.033 | 16.332 | 0.000 | 0.478 | 0.609 |
| UN | =~ | un04 | 0.735 | 0.022 | 32.798 | 0.000 | 0.691 | 0.779 |
| UN | =~ | un05 | 0.806 | 0.018 | 45.760 | 0.000 | 0.771 | 0.840 |
| UN | =~ | un06 | 0.494 | 0.036 | 13.895 | 0.000 | 0.425 | 0.564 |
| UN | =~ | un07 | 0.687 | 0.025 | 27.059 | 0.000 | 0.638 | 0.737 |
| UN | =~ | un08 | 0.752 | 0.021 | 35.289 | 0.000 | 0.710 | 0.794 |
| UN | =~ | un09 | 0.690 | 0.025 | 27.358 | 0.000 | 0.641 | 0.740 |
| UN | =~ | un10 | 0.710 | 0.024 | 29.592 | 0.000 | 0.663 | 0.757 |
| UN | =~ | un11 | 0.772 | 0.020 | 38.620 | 0.000 | 0.732 | 0.811 |
| UN | =~ | un12 | 0.737 | 0.022 | 33.023 | 0.000 | 0.693 | 0.780 |
Covariances:
kable(
smodel1 %>%
filter(op == "~~" & lhs != rhs),
col.names = c("Factor", "", "Factor", "Covariance", "SE", "z", "p", "CI lower bound", "CI upper bound"),
digits = 3
)| Factor | Factor | Covariance | SE | z | p | CI lower bound | CI upper bound | |
|---|---|---|---|---|---|---|---|---|
| PR | ~~ | CO | 0.674 | 0.032 | 21.123 | 0.000 | 0.611 | 0.736 |
| PR | ~~ | UT | 0.758 | 0.025 | 29.745 | 0.000 | 0.708 | 0.808 |
| PR | ~~ | FA | 0.572 | 0.037 | 15.251 | 0.000 | 0.498 | 0.645 |
| PR | ~~ | DE | 0.886 | 0.019 | 47.788 | 0.000 | 0.849 | 0.922 |
| PR | ~~ | UN | 0.383 | 0.043 | 8.811 | 0.000 | 0.298 | 0.469 |
| CO | ~~ | UT | 0.434 | 0.042 | 10.255 | 0.000 | 0.351 | 0.517 |
| CO | ~~ | FA | 0.493 | 0.041 | 12.022 | 0.000 | 0.413 | 0.574 |
| CO | ~~ | DE | 0.667 | 0.033 | 20.416 | 0.000 | 0.603 | 0.731 |
| CO | ~~ | UN | 0.276 | 0.047 | 5.929 | 0.000 | 0.185 | 0.367 |
| UT | ~~ | FA | 0.451 | 0.042 | 10.813 | 0.000 | 0.369 | 0.532 |
| UT | ~~ | DE | 0.684 | 0.030 | 22.435 | 0.000 | 0.624 | 0.744 |
| UT | ~~ | UN | 0.296 | 0.045 | 6.570 | 0.000 | 0.207 | 0.384 |
| FA | ~~ | DE | 0.659 | 0.033 | 19.845 | 0.000 | 0.594 | 0.724 |
| FA | ~~ | UN | 0.087 | 0.050 | 1.762 | 0.078 | -0.010 | 0.185 |
| DE | ~~ | UN | 0.389 | 0.044 | 8.920 | 0.000 | 0.304 | 0.475 |
Residuals:
kable(
smodel1 %>%
filter(op == "~~" & lhs == rhs)%>%
select(-(2:3)),
col.names = c("Item", "Residual", "SE", "z", "p", "CI lower bound", "CI upper bound"),
digits = 3
)| Item | Residual | SE | z | p | CI lower bound | CI upper bound |
|---|---|---|---|---|---|---|
| pr01 | 0.377 | 0.031 | 12.076 | 0 | 0.316 | 0.438 |
| pr02 | 0.659 | 0.037 | 17.653 | 0 | 0.586 | 0.732 |
| pr03 | 0.912 | 0.026 | 35.640 | 0 | 0.862 | 0.962 |
| pr04 | 0.987 | 0.011 | 92.749 | 0 | 0.966 | 1.008 |
| pr05 | 0.681 | 0.037 | 18.291 | 0 | 0.608 | 0.754 |
| pr06 | 0.655 | 0.037 | 17.542 | 0 | 0.582 | 0.728 |
| pr07 | 0.470 | 0.035 | 13.609 | 0 | 0.402 | 0.537 |
| pr08 | 0.473 | 0.035 | 13.667 | 0 | 0.405 | 0.541 |
| pr09 | 0.661 | 0.037 | 17.694 | 0 | 0.587 | 0.734 |
| pr10 | 0.750 | 0.036 | 20.839 | 0 | 0.680 | 0.821 |
| co01 | 0.454 | 0.036 | 12.730 | 0 | 0.384 | 0.523 |
| co02 | 0.530 | 0.037 | 14.153 | 0 | 0.456 | 0.603 |
| co03 | 0.747 | 0.037 | 20.238 | 0 | 0.675 | 0.820 |
| co04 | 0.887 | 0.029 | 30.728 | 0 | 0.830 | 0.943 |
| co05 | 0.390 | 0.034 | 11.593 | 0 | 0.324 | 0.456 |
| co06 | 0.622 | 0.038 | 16.181 | 0 | 0.546 | 0.697 |
| co08 | 0.887 | 0.029 | 30.806 | 0 | 0.831 | 0.944 |
| co09 | 0.469 | 0.036 | 13.014 | 0 | 0.399 | 0.540 |
| co10 | 0.636 | 0.038 | 16.536 | 0 | 0.560 | 0.711 |
| ut01 | 0.402 | 0.032 | 12.630 | 0 | 0.340 | 0.464 |
| ut02 | 0.301 | 0.027 | 11.006 | 0 | 0.247 | 0.354 |
| ut03 | 0.750 | 0.036 | 20.907 | 0 | 0.679 | 0.820 |
| ut04 | 0.783 | 0.035 | 22.500 | 0 | 0.715 | 0.851 |
| ut05 | 0.579 | 0.037 | 15.774 | 0 | 0.507 | 0.651 |
| ut06 | 0.441 | 0.033 | 13.251 | 0 | 0.376 | 0.506 |
| ut07 | 0.678 | 0.037 | 18.290 | 0 | 0.605 | 0.751 |
| ut08 | 0.611 | 0.037 | 16.492 | 0 | 0.538 | 0.683 |
| ut09 | 0.568 | 0.037 | 15.554 | 0 | 0.497 | 0.640 |
| ut11 | 0.729 | 0.036 | 20.063 | 0 | 0.658 | 0.801 |
| ut12 | 0.524 | 0.036 | 14.683 | 0 | 0.454 | 0.594 |
| fa01 | 0.325 | 0.032 | 10.177 | 0 | 0.262 | 0.387 |
| fa02 | 0.832 | 0.033 | 25.070 | 0 | 0.767 | 0.897 |
| fa03 | 0.969 | 0.016 | 59.581 | 0 | 0.938 | 1.001 |
| fa04 | 0.875 | 0.030 | 29.210 | 0 | 0.816 | 0.934 |
| fa05 | 0.287 | 0.031 | 9.357 | 0 | 0.227 | 0.347 |
| fa06 | 0.640 | 0.039 | 16.624 | 0 | 0.565 | 0.716 |
| fa07 | 0.922 | 0.025 | 37.218 | 0 | 0.874 | 0.971 |
| fa08 | 0.849 | 0.032 | 26.527 | 0 | 0.787 | 0.912 |
| fa09 | 0.770 | 0.036 | 21.268 | 0 | 0.699 | 0.841 |
| fa10 | 0.649 | 0.038 | 16.872 | 0 | 0.574 | 0.725 |
| de01 | 0.686 | 0.037 | 18.318 | 0 | 0.612 | 0.759 |
| de02 | 0.510 | 0.036 | 14.162 | 0 | 0.439 | 0.580 |
| de03 | 0.629 | 0.038 | 16.722 | 0 | 0.555 | 0.703 |
| de05 | 0.731 | 0.037 | 19.910 | 0 | 0.659 | 0.803 |
| de06 | 0.592 | 0.037 | 15.832 | 0 | 0.519 | 0.665 |
| de07 | 0.637 | 0.038 | 16.934 | 0 | 0.564 | 0.711 |
| de08 | 0.524 | 0.036 | 14.436 | 0 | 0.453 | 0.595 |
| de09 | 0.904 | 0.027 | 33.919 | 0 | 0.852 | 0.956 |
| de10 | 0.487 | 0.035 | 13.746 | 0 | 0.417 | 0.556 |
| de11 | 0.941 | 0.022 | 43.330 | 0 | 0.898 | 0.984 |
| un01 | 0.453 | 0.033 | 13.854 | 0 | 0.389 | 0.517 |
| un02 | 0.309 | 0.026 | 11.747 | 0 | 0.258 | 0.361 |
| un03 | 0.705 | 0.036 | 19.471 | 0 | 0.634 | 0.775 |
| un04 | 0.460 | 0.033 | 13.962 | 0 | 0.395 | 0.524 |
| un05 | 0.350 | 0.028 | 12.343 | 0 | 0.295 | 0.406 |
| un06 | 0.756 | 0.035 | 21.495 | 0 | 0.687 | 0.825 |
| un07 | 0.528 | 0.035 | 15.106 | 0 | 0.459 | 0.596 |
| un08 | 0.435 | 0.032 | 13.574 | 0 | 0.372 | 0.498 |
| un09 | 0.524 | 0.035 | 15.035 | 0 | 0.455 | 0.592 |
| un10 | 0.496 | 0.034 | 14.549 | 0 | 0.429 | 0.563 |
| un11 | 0.405 | 0.031 | 13.125 | 0 | 0.344 | 0.465 |
| un12 | 0.458 | 0.033 | 13.925 | 0 | 0.393 | 0.522 |
| PR | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
| CO | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
| UT | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
| FA | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
| DE | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
| UN | 1.000 | 0.000 | NA | NA | 1.000 | 1.000 |
Visualization:
Model:
mdl2 <- "
PR =~ pr01 + pr02 + pr03 + pr04 + pr05 + pr06 + pr07 + pr08 + pr09 + pr10
CO =~ co01 + co02 + co03 + co04 + co05 + co06 + co08 + co09 + co10
UT =~ ut01 + ut02 + ut03 + ut04 + ut05 + ut06 + ut07 + ut08 + ut09 + ut11 + ut12
FA =~ fa01 + fa02 + fa03 + fa04 + fa05 + fa06 + fa07 + fa08 + fa09 + fa10
DE =~ de01 + de02 + de03 + de05 + de06 + de07 + de08 + de09 + de10 + de11
UN =~ un01 + un02 + un03 + un04 + un05 + un06 + un07 + un08 + un09 + un10 + un11 + un12
DigTrust =~ PR + CO + UT + FA + DE + UN
"## lavaan 0.6-8 ended normally after 44 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 130
##
## Number of observations 495
##
## Model Test User Model:
##
## Test statistic 6381.193
## Degrees of freedom 1823
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## PR =~
## pr01 1.000
## pr02 0.732 0.055 13.227 0.000
## pr03 0.389 0.061 6.409 0.000
## pr04 0.160 0.063 2.533 0.011
## pr05 0.882 0.069 12.845 0.000
## pr06 0.797 0.061 13.006 0.000
## pr07 1.049 0.061 17.137 0.000
## pr08 0.846 0.050 16.877 0.000
## pr09 0.719 0.055 13.186 0.000
## pr10 0.661 0.060 10.962 0.000
## CO =~
## co01 1.000
## co02 0.894 0.062 14.491 0.000
## co03 0.652 0.061 10.699 0.000
## co04 0.473 0.065 7.263 0.000
## co05 1.088 0.066 16.548 0.000
## co06 0.862 0.066 13.089 0.000
## co08 0.435 0.062 6.976 0.000
## co09 0.977 0.063 15.442 0.000
## co10 0.834 0.065 12.798 0.000
## UT =~
## ut01 1.000
## ut02 1.080 0.055 19.757 0.000
## ut03 0.674 0.062 10.910 0.000
## ut04 0.635 0.062 10.237 0.000
## ut05 0.972 0.065 14.837 0.000
## ut06 1.007 0.058 17.336 0.000
## ut07 0.791 0.062 12.699 0.000
## ut08 0.807 0.057 14.089 0.000
## ut09 0.946 0.063 14.941 0.000
## ut11 0.790 0.068 11.534 0.000
## ut12 0.973 0.062 15.774 0.000
## FA =~
## fa01 1.000
## fa02 0.522 0.060 8.760 0.000
## fa03 0.204 0.059 3.452 0.001
## fa04 0.407 0.055 7.352 0.000
## fa05 1.017 0.050 20.447 0.000
## fa06 0.704 0.052 13.544 0.000
## fa07 0.323 0.056 5.749 0.000
## fa08 0.475 0.058 8.239 0.000
## fa09 0.616 0.059 10.488 0.000
## fa10 0.780 0.058 13.496 0.000
## DE =~
## de01 1.000
## de02 1.314 0.113 11.628 0.000
## de03 1.176 0.111 10.576 0.000
## de05 1.006 0.104 9.645 0.000
## de06 1.267 0.116 10.914 0.000
## de07 0.989 0.093 10.609 0.000
## de08 1.188 0.103 11.539 0.000
## de09 0.623 0.098 6.342 0.000
## de10 1.371 0.117 11.764 0.000
## de11 0.475 0.096 4.934 0.000
## UN =~
## un01 1.000
## un02 1.212 0.064 18.935 0.000
## un03 0.811 0.068 11.917 0.000
## un04 1.022 0.062 16.530 0.000
## un05 1.140 0.062 18.326 0.000
## un06 0.780 0.072 10.831 0.000
## un07 1.035 0.067 15.346 0.000
## un08 1.118 0.066 16.974 0.000
## un09 1.085 0.070 15.412 0.000
## un10 1.046 0.066 15.902 0.000
## un11 1.188 0.068 17.430 0.000
## un12 1.061 0.064 16.562 0.000
## DigTrust =~
## PR 1.000
## CO 0.746 0.061 12.327 0.000
## UT 0.818 0.060 13.693 0.000
## FA 0.781 0.064 12.133 0.000
## DE 0.777 0.066 11.697 0.000
## UN 0.408 0.053 7.651 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .pr01 0.369 0.028 13.031 0.000
## .pr02 0.616 0.041 14.883 0.000
## .pr03 0.935 0.060 15.576 0.000
## .pr04 1.070 0.068 15.709 0.000
## .pr05 0.966 0.065 14.947 0.000
## .pr06 0.763 0.051 14.921 0.000
## .pr07 0.573 0.041 13.836 0.000
## .pr08 0.393 0.028 13.938 0.000
## .pr09 0.599 0.040 14.890 0.000
## .pr10 0.810 0.053 15.208 0.000
## .co01 0.528 0.040 13.119 0.000
## .co02 0.578 0.042 13.832 0.000
## .co03 0.775 0.052 14.982 0.000
## .co04 1.037 0.067 15.438 0.000
## .co05 0.485 0.039 12.362 0.000
## .co06 0.759 0.053 14.391 0.000
## .co08 0.959 0.062 15.463 0.000
## .co09 0.539 0.041 13.290 0.000
## .co10 0.761 0.053 14.482 0.000
## .ut01 0.442 0.033 13.486 0.000
## .ut02 0.336 0.027 12.276 0.000
## .ut03 0.925 0.061 15.248 0.000
## .ut04 0.959 0.063 15.318 0.000
## .ut05 0.836 0.057 14.615 0.000
## .ut06 0.528 0.038 13.826 0.000
## .ut07 0.864 0.058 15.017 0.000
## .ut08 0.673 0.046 14.776 0.000
## .ut09 0.775 0.053 14.590 0.000
## .ut11 1.106 0.073 15.176 0.000
## .ut12 0.690 0.048 14.373 0.000
## .fa01 0.375 0.035 10.619 0.000
## .fa02 1.162 0.076 15.307 0.000
## .fa03 1.250 0.080 15.672 0.000
## .fa04 1.036 0.067 15.443 0.000
## .fa05 0.348 0.035 10.076 0.000
## .fa06 0.748 0.052 14.517 0.000
## .fa07 1.097 0.070 15.560 0.000
## .fa08 1.100 0.072 15.361 0.000
## .fa09 1.074 0.071 15.090 0.000
## .fa10 0.927 0.064 14.529 0.000
## .de01 0.823 0.055 14.925 0.000
## .de02 0.681 0.049 14.028 0.000
## .de03 0.904 0.061 14.713 0.000
## .de05 0.968 0.064 15.039 0.000
## .de06 0.902 0.062 14.545 0.000
## .de07 0.630 0.043 14.698 0.000
## .de08 0.583 0.041 14.109 0.000
## .de09 1.288 0.083 15.533 0.000
## .de10 0.687 0.049 13.891 0.000
## .de11 1.361 0.087 15.623 0.000
## .un01 0.497 0.035 14.367 0.000
## .un02 0.399 0.030 13.228 0.000
## .un03 0.966 0.063 15.272 0.000
## .un04 0.541 0.038 14.422 0.000
## .un05 0.423 0.031 13.641 0.000
## .un06 1.148 0.075 15.374 0.000
## .un07 0.732 0.050 14.740 0.000
## .un08 0.581 0.041 14.271 0.000
## .un09 0.792 0.054 14.725 0.000
## .un10 0.657 0.045 14.604 0.000
## .un11 0.584 0.041 14.091 0.000
## .un12 0.578 0.040 14.412 0.000
## .PR 0.051 0.017 2.954 0.003
## .CO 0.324 0.039 8.231 0.000
## .UT 0.290 0.033 8.734 0.000
## .FA 0.498 0.051 9.744 0.000
## .DE 0.043 0.012 3.438 0.001
## .UN 0.516 0.055 9.336 0.000
## DigTrust 0.552 0.058 9.531 0.000
kable(tibble(
`Model 2` = c(
"Chi-Squared",
"DF",
"p",
"GFI",
"AGFI",
"CFI",
"TLI",
"SRMR",
"RMSEA"
),
Value = round(fitmeasures(
model2,
c(
"chisq",
"df",
"pvalue",
"gfi",
"agfi",
"cfi",
"tli",
"srmr",
"rmsea"
)
), 4)
))| Model 2 | Value |
|---|---|
| Chi-Squared | 6381.1932 |
| DF | 1823.0000 |
| p | 0.0000 |
| GFI | 0.6329 |
| AGFI | 0.6068 |
| CFI | 0.7067 |
| TLI | 0.6957 |
| SRMR | 0.1029 |
| RMSEA | 0.0711 |
taia %>%
select(id, all_of(taia_items)) %>%
pivot_longer(all_of(taia_items),
names_to = "subscale",
values_to = "score") %>%
mutate(subscale = str_remove_all(subscale, "[:digit:]{2}") %>% toupper()) %>%
group_by(id, subscale) %>%
summarise(total_score = sum(score)) %>%
pivot_wider(id_cols = id,
names_from = subscale,
values_from = total_score) %>%
full_join(taia) -> taia## `summarise()` regrouping output by 'id' (override with `.groups` argument)
## Joining, by = "id"
taia %>%
pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
names_to = "subscale",
values_to = "score") %>%
mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) -> taia_ltaia_l %>%
ggplot(aes(score, gt_score, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
scale_color_manual(values = clrs) +
guides(color = FALSE) +
labs(x = "TAIA subscale total score",
y = "General Trust Scale total score",
title = "Corelations between General Trust and TAIA subscales") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'
##
## Pearson's product-moment correlation
##
## data: taia$PR and taia$gt_score
## t = 3.1872, df = 493, p-value = 0.001528
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.05463758 0.22737163
## sample estimates:
## cor
## 0.1420861
##
## Pearson's product-moment correlation
##
## data: taia$CO and taia$gt_score
## t = 3.842, df = 493, p-value = 0.000138
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.08362567 0.25480562
## sample estimates:
## cor
## 0.1705018
##
## Pearson's product-moment correlation
##
## data: taia$UT and taia$gt_score
## t = 2.4679, df = 493, p-value = 0.01393
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.02255584 0.19668679
## sample estimates:
## cor
## 0.110469
##
## Pearson's product-moment correlation
##
## data: taia$FA and taia$gt_score
## t = 2.0938, df = 493, p-value = 0.03678
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.005800447 0.180524218
## sample estimates:
## cor
## 0.0938852
##
## Pearson's product-moment correlation
##
## data: taia$DE and taia$gt_score
## t = 4.1369, df = 493, p-value = 4.139e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.09659152 0.26698780
## sample estimates:
## cor
## 0.183165
##
## Pearson's product-moment correlation
##
## data: taia$UN and taia$gt_score
## t = 3.5643, df = 493, p-value = 0.0004003
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.07136383 0.24323469
## sample estimates:
## cor
## 0.1584997
taia_l %>%
ggplot(aes(score, n_dighelp, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Number of digital helpers",
title = "Correlation TAIA subscales with number of digital helpers") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 948 rows containing non-finite values (stat_smooth).
## Warning: Removed 948 rows containing missing values (geom_point).
## Warning in cor.test.default(taia$PR, taia$n_dighelp, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$PR and taia$n_dighelp
## S = 5561417, p-value = 0.01861
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1281319
## Warning in cor.test.default(taia$CO, taia$n_dighelp, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$CO and taia$n_dighelp
## S = 6618523, p-value = 0.4916
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.03759163
## Warning in cor.test.default(taia$UT, taia$n_dighelp, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$UT and taia$n_dighelp
## S = 5620719, p-value = 0.02917
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.118835
## Warning in cor.test.default(taia$FA, taia$n_dighelp, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$FA and taia$n_dighelp
## S = 6614475, p-value = 0.4989
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.03695705
## Warning in cor.test.default(taia$DE, taia$n_dighelp, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$DE and taia$n_dighelp
## S = 5705718, p-value = 0.05298
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1055096
## Warning in cor.test.default(taia$UN, taia$n_dighelp, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$UN and taia$n_dighelp
## S = 5912050, p-value = 0.1803
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.0731627
taia_l %>%
ggplot(aes(score, e_dighelp, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Estimate of dealing with digital helpers experience",
title = "Correlation TAIA subscales with expirience of dealing with digital helpers") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 948 rows containing non-finite values (stat_smooth).
## Warning: Removed 948 rows containing missing values (geom_point).
##
## Pearson's product-moment correlation
##
## data: taia$PR and taia$e_dighelp
## t = 6.7852, df = 335, p-value = 5.27e-11
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2500476 0.4381609
## sample estimates:
## cor
## 0.3475971
##
## Pearson's product-moment correlation
##
## data: taia$CO and taia$e_dighelp
## t = 4.0996, df = 335, p-value = 5.199e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1144032 0.3179773
## sample estimates:
## cor
## 0.218567
##
## Pearson's product-moment correlation
##
## data: taia$UT and taia$e_dighelp
## t = 5.5088, df = 335, p-value = 7.21e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1871349 0.3832429
## sample estimates:
## cor
## 0.288208
##
## Pearson's product-moment correlation
##
## data: taia$FA and taia$e_dighelp
## t = 4.1342, df = 335, p-value = 4.507e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1162249 0.3196358
## sample estimates:
## cor
## 0.2203243
##
## Pearson's product-moment correlation
##
## data: taia$DE and taia$e_dighelp
## t = 5.7293, df = 335, p-value = 2.248e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1982204 0.3930214
## sample estimates:
## cor
## 0.2987294
##
## Pearson's product-moment correlation
##
## data: taia$UN and taia$e_dighelp
## t = 1.7091, df = 335, p-value = 0.08836
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.01400204 0.19784231
## sample estimates:
## cor
## 0.09297223
taia_l %>%
ggplot(aes(score, n_socnet, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Number of social networks and social media",
title = "Correlation TAIA subscales with number of social networks and social media") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 360 rows containing non-finite values (stat_smooth).
## Warning: Removed 360 rows containing missing values (geom_point).
## Warning in cor.test.default(taia$PR, taia$n_socnet, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$PR and taia$n_socnet
## S = 12147347, p-value = 0.01685
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1145436
## Warning in cor.test.default(taia$CO, taia$n_socnet, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$CO and taia$n_socnet
## S = 14058484, p-value = 0.6065
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.02476493
## Warning in cor.test.default(taia$UT, taia$n_socnet, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$UT and taia$n_socnet
## S = 11573855, p-value = 0.001069
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1563471
## Warning in cor.test.default(taia$FA, taia$n_socnet, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$FA and taia$n_socnet
## S = 12379302, p-value = 0.04181
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.09763564
## Warning in cor.test.default(taia$DE, taia$n_socnet, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$DE and taia$n_socnet
## S = 11425218, p-value = 0.0004627
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1671817
## Warning in cor.test.default(taia$UN, taia$n_socnet, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$UN and taia$n_socnet
## S = 12007801, p-value = 0.009219
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1247154
taia_l %>%
ggplot(aes(score, f_socnet, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Frequency of social networks and social media use",
title = "Correlation TAIA subscales with frequency of social networks and social media use") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 360 rows containing non-finite values (stat_smooth).
## Warning: Removed 360 rows containing missing values (geom_point).
##
## Pearson's product-moment correlation
##
## data: taia$PR and taia$f_socnet
## t = 1.4848, df = 433, p-value = 0.1383
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02299864 0.16409771
## sample estimates:
## cor
## 0.07117555
##
## Pearson's product-moment correlation
##
## data: taia$CO and taia$f_socnet
## t = 0.83383, df = 433, p-value = 0.4048
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.05418487 0.13355691
## sample estimates:
## cor
## 0.0400394
##
## Pearson's product-moment correlation
##
## data: taia$UT and taia$f_socnet
## t = -0.18344, df = 433, p-value = 0.8545
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.10275052 0.08527558
## sample estimates:
## cor
## -0.008815392
##
## Pearson's product-moment correlation
##
## data: taia$FA and taia$f_socnet
## t = 0.30431, df = 433, p-value = 0.761
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.07950679 0.10849394
## sample estimates:
## cor
## 0.01462281
##
## Pearson's product-moment correlation
##
## data: taia$DE and taia$f_socnet
## t = 0.51348, df = 433, p-value = 0.6079
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.06951288 0.11841428
## sample estimates:
## cor
## 0.02466864
##
## Pearson's product-moment correlation
##
## data: taia$UN and taia$f_socnet
## t = -0.29625, df = 433, p-value = 0.7672
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.10811106 0.07989175
## sample estimates:
## cor
## -0.01423547
taia_l %>%
ggplot(aes(score, e_socnet, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Estimate of dealing with recommender systems experience",
title = "Correlation TAIA subscales with experience of dealing with recommender systems") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 360 rows containing non-finite values (stat_smooth).
## Warning: Removed 360 rows containing missing values (geom_point).
##
## Pearson's product-moment correlation
##
## data: taia$PR and taia$e_socnet
## t = 4.6683, df = 433, p-value = 4.056e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1275089 0.3066146
## sample estimates:
## cor
## 0.2189049
##
## Pearson's product-moment correlation
##
## data: taia$CO and taia$e_socnet
## t = 5.3414, df = 433, p-value = 1.493e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1583093 0.3348224
## sample estimates:
## cor
## 0.2486289
##
## Pearson's product-moment correlation
##
## data: taia$UT and taia$e_socnet
## t = 4.0939, df = 433, p-value = 5.061e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1008503 0.2819433
## sample estimates:
## cor
## 0.1930402
##
## Pearson's product-moment correlation
##
## data: taia$FA and taia$e_socnet
## t = 2.5727, df = 433, p-value = 0.01042
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.02901732 0.21425142
## sample estimates:
## cor
## 0.1227028
##
## Pearson's product-moment correlation
##
## data: taia$DE and taia$e_socnet
## t = 5.7293, df = 433, p-value = 1.89e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1758227 0.3507217
## sample estimates:
## cor
## 0.2654548
##
## Pearson's product-moment correlation
##
## data: taia$UN and taia$e_socnet
## t = 1.7866, df = 433, p-value = 0.0747
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.008545219 0.178131408
## sample estimates:
## cor
## 0.0855438
taia %>%
pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
names_to = "subscale",
values_to = "score") %>%
mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) %>%
ggplot(aes(score, selfdrexp, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Estimate of selfdriving car experience",
title = "Correlation TAIA subscales with selfdriving car experience") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 2892 rows containing non-finite values (stat_smooth).
## Warning: Removed 2892 rows containing missing values (geom_point).
## Warning in cor.test.default(taia$PR, taia$selfdrexp, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$PR and taia$selfdrexp
## S = 265.3, p-value = 0.3702
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2711565
## Warning in cor.test.default(taia$CO, taia$selfdrexp, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$CO and taia$selfdrexp
## S = 418.25, p-value = 0.627
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.1490473
## Warning in cor.test.default(taia$UT, taia$selfdrexp, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$UT and taia$selfdrexp
## S = 175.48, p-value = 0.06984
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.5179022
## Warning in cor.test.default(taia$FA, taia$selfdrexp, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$FA and taia$selfdrexp
## S = 272.56, p-value = 0.4077
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2512199
## Warning in cor.test.default(taia$DE, taia$selfdrexp, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$DE and taia$selfdrexp
## S = 326.24, p-value = 0.7359
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1037321
## Warning in cor.test.default(taia$UN, taia$selfdrexp, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$UN and taia$selfdrexp
## S = 358.61, p-value = 0.9617
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.01481886
taia %>%
pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
names_to = "subscale",
values_to = "score") %>%
mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) %>%
ggplot(aes(score, selfdrsafe, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Estimate of selfdriving car safe",
title = "Correlation TAIA subscales with selfdriving car safe") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 2892 rows containing non-finite values (stat_smooth).
## Warning: Removed 2892 rows containing missing values (geom_point).
## Warning in cor.test.default(taia$PR, taia$selfdrsafe, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$PR and taia$selfdrsafe
## S = 372.22, p-value = 0.9417
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.02257133
## Warning in cor.test.default(taia$CO, taia$selfdrsafe, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$CO and taia$selfdrsafe
## S = 323.96, p-value = 0.7205
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1099992
## Warning in cor.test.default(taia$UT, taia$selfdrsafe, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$UT and taia$selfdrsafe
## S = 301.67, p-value = 0.576
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1712239
## Warning in cor.test.default(taia$FA, taia$selfdrsafe, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$FA and taia$selfdrsafe
## S = 217.35, p-value = 0.1723
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.4028841
## Warning in cor.test.default(taia$DE, taia$selfdrsafe, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$DE and taia$selfdrsafe
## S = 352.98, p-value = 0.9218
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.03026276
## Warning in cor.test.default(taia$UN, taia$selfdrsafe, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$UN and taia$selfdrsafe
## S = 450.47, p-value = 0.4345
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.2375627
taia %>%
pivot_longer(cols = c("PR", "CO", "UT", "FA", "DE", "UN"),
names_to = "subscale",
values_to = "score") %>%
mutate(subscale = factor(subscale, levels = c("PR", "CO", "UT", "FA", "DE", "UN"))) %>%
ggplot(aes(score, eduaiexp, color = subscale)) +
geom_point(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~ subscale) +
guides(color = FALSE) +
scale_color_manual(values = clrs) +
labs(x = "TAIA subscales total score",
y = "Estimate of dealing with education AI experience",
title = "Correlation TAIA subscales with experience of dealing with education AI") +
theme(plot.title = element_text(hjust = .5))## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 2298 rows containing non-finite values (stat_smooth).
## Warning: Removed 2298 rows containing missing values (geom_point).
## Warning in cor.test.default(taia$PR, taia$eduaiexp, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$PR and taia$eduaiexp
## S = 136920, p-value = 5.31e-06
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.4152137
## Warning in cor.test.default(taia$CO, taia$eduaiexp, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$CO and taia$eduaiexp
## S = 145289, p-value = 3.687e-05
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3794657
## Warning in cor.test.default(taia$UT, taia$eduaiexp, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$UT and taia$eduaiexp
## S = 142744, p-value = 2.094e-05
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3903391
## Warning in cor.test.default(taia$FA, taia$eduaiexp, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$FA and taia$eduaiexp
## S = 168570, p-value = 0.002786
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2800356
## Warning in cor.test.default(taia$DE, taia$eduaiexp, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$DE and taia$eduaiexp
## S = 112949, p-value = 5.1e-09
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.5175924
## Warning in cor.test.default(taia$UN, taia$eduaiexp, method = "sp"): Cannot
## compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: taia$UN and taia$eduaiexp
## S = 159518, p-value = 0.0006156
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3186946